
import { RouteConfig } from '@/types/router';
const menus = [
        {
            "id": 1,
            "parent_id": 0,
            "title": "智能对话",
            "path": "/chat",
            "disabled": 0,
            "hidden": 0,
            "order": 0,
            "menu_type": null,
            "perms": "chat::select",
            "create_datetime": "2025-03-18T13:48:44",
            "update_datetime": "2025-03-18T13:48:44"
        },
        {
            "id": 2,
            "parent_id": 0,
            "title": "应用广场",
            "path": "/home",
            "disabled": 0,
            "hidden": 0,
            "order": 2,
            "menu_type": null,
            "perms": null,
            "create_datetime": "2025-03-18T13:48:44",
            "update_datetime": "2025-03-18T13:48:44"
        },
        {
            "id": 3,
            "parent_id": 0,
            "title": "应用管理",
            "path": "/construct",
            "disabled": 0,
            "hidden": 0,
            "order": 3,
            "menu_type": null,
            "perms": null,
            "create_datetime": "2025-03-18T13:48:45",
            "update_datetime": "2025-03-18T13:48:45",
            "children": [
                {
                    "id": 4,
                    "parent_id": 3,
                    "title": "应用程序",
                    "path": "/construct/app",
                    "disabled": 0,
                    "hidden": 0,
                    "order": 0,
                    "menu_type": null,
                    "perms": null,
                    "create_datetime": "2025-03-18T13:48:45",
                    "update_datetime": "2025-03-18T13:48:45",
                    "children": [
                        {
                            "id": 16,
                            "parent_id": 4,
                            "title": "新增",
                            "path": "/app/extra",
                            "disabled": 0,
                            "hidden": 0,
                            "order": 1,
                            "menu_type": null,
                            "perms": null,
                            "create_datetime": "2025-03-18T13:48:45",
                            "update_datetime": "2025-03-18T13:48:45"
                        }
                    ]
                },
                {
                    "id": 5,
                    "parent_id": 3,
                    "title": "工作流",
                    "path": "/construct/flow",
                    "disabled": 0,
                    "hidden": 0,
                    "order": 1,
                    "menu_type": null,
                    "perms": null,
                    "create_datetime": "2025-03-18T13:48:45",
                    "update_datetime": "2025-03-18T13:48:45",
                    "children": [
                        {
                            "id": 17,
                            "parent_id": 5,
                            "title": "新增",
                            "path": "/construct/flow/canvas",
                            "disabled": 0,
                            "hidden": 0,
                            "order": 1,
                            "menu_type": null,
                            "perms": null,
                            "create_datetime": "2025-03-21T16:55:13",
                            "update_datetime": "2025-03-21T16:55:13"
                        }
                    ]
                },
                {
                    "id": 6,
                    "parent_id": 3,
                    "title": "模型管理",
                    "path": "/construct/models",
                    "disabled": 0,
                    "hidden": 0,
                    "order": 2,
                    "menu_type": null,
                    "perms": null,
                    "create_datetime": "2025-03-18T13:48:45",
                    "update_datetime": "2025-03-18T13:48:45"
                },
                {
                    "id": 7,
                    "parent_id": 3,
                    "title": "数据库",
                    "path": "/construct/database",
                    "disabled": 0,
                    "hidden": 0,
                    "order": 3,
                    "menu_type": null,
                    "perms": null,
                    "create_datetime": "2025-03-18T13:48:45",
                    "update_datetime": "2025-03-18T13:48:45"
                },
                {
                    "id": 8,
                    "parent_id": 3,
                    "title": "知识库",
                    "path": "/construct/knowledge",
                    "disabled": 0,
                    "hidden": 0,
                    "order": 4,
                    "menu_type": null,
                    "perms": null,
                    "create_datetime": "2025-03-18T13:48:45",
                    "update_datetime": "2025-03-18T13:48:45",
                    "children": [
                        {
                            "id": 18,
                            "parent_id": 8,
                            "title": "知识图谱",
                            "path": "/knowledge/graph",
                            "disabled": 0,
                            "hidden": 0,
                            "order": 1,
                            "menu_type": null,
                            "perms": null,
                            "create_datetime": "2025-03-27T11:26:33",
                            "update_datetime": "2025-03-27T11:26:33"
                        },
                        {
                            "id": 19,
                            "parent_id": 8,
                            "title": "详情",
                            "path": "/construct/knowledge/chunk",
                            "disabled": 0,
                            "hidden": 0,
                            "order": 1,
                            "menu_type": null,
                            "perms": null,
                            "create_datetime": "2025-03-28T13:56:43",
                            "update_datetime": "2025-03-28T13:56:43"
                        }
                    ]
                },
                {
                    "id": 23,
                    "parent_id": 3,
                    "title": "领域词库",
                    "path": "/domainThesaurus",
                    "disabled": 0,
                    "hidden": 0,
                    "order": 5,
                    "menu_type": null,
                    "perms": null,
                    "create_datetime": "2025-06-05T16:48:57",
                    "update_datetime": "2025-06-05T16:48:57"
                },
                {
                    "id": 9,
                    "parent_id": 3,
                    "title": "提示词",
                    "path": "/construct/prompt",
                    "disabled": 0,
                    "hidden": 0,
                    "order": 6,
                    "menu_type": null,
                    "perms": null,
                    "create_datetime": "2025-03-18T13:48:45",
                    "update_datetime": "2025-03-18T13:48:45",
                    "children": [
                        {
                            "id": 15,
                            "parent_id": 9,
                            "title": "编辑",
                            "path": "/construct/prompt/[type]",
                            "disabled": 0,
                            "hidden": 0,
                            "order": 1,
                            "menu_type": null,
                            "perms": null,
                            "create_datetime": "2025-03-18T13:48:45",
                            "update_datetime": "2025-03-18T13:48:45"
                        }
                    ]
                },
                {
                    "id": 24,
                    "parent_id": 3,
                    "title": "MCP管理",
                    "path": "/construct/mcp",
                    "disabled": 0,
                    "hidden": 0,
                    "order": 8,
                    "menu_type": null,
                    "perms": null,
                    "create_datetime": "2025-06-19T11:24:04",
                    "update_datetime": "2025-06-19T11:24:04"
                },
                {
                    "id": 11,
                    "parent_id": 3,
                    "title": "关于Noah.X",
                    "path": "/construct/about",
                    "disabled": 0,
                    "hidden": 0,
                    "order": 9,
                    "menu_type": null,
                    "perms": null,
                    "create_datetime": "2025-03-18T13:48:45",
                    "update_datetime": "2025-03-18T13:48:45"
                }
            ]
        },
        {
            "id": 20,
            "parent_id": 0,
            "title": "工作台",
            "path": "/workbenches",
            "disabled": 0,
            "hidden": 0,
            "order": 4,
            "menu_type": null,
            "perms": null,
            "create_datetime": "2025-04-15T10:02:03",
            "update_datetime": "2025-04-15T10:02:03"
        },
        {
            "id": 12,
            "parent_id": 0,
            "title": "系统管理",
            "path": "/system",
            "disabled": 0,
            "hidden": 0,
            "order": 5,
            "menu_type": null,
            "perms": null,
            "create_datetime": "2025-03-18T13:48:45",
            "update_datetime": "2025-03-18T13:48:45",
            "children": [
                {
                    "id": 13,
                    "parent_id": 12,
                    "title": "用户",
                    "path": "/system/user",
                    "disabled": 0,
                    "hidden": 0,
                    "order": 0,
                    "menu_type": null,
                    "perms": null,
                    "create_datetime": "2025-03-18T13:48:45",
                    "update_datetime": "2025-03-18T13:48:45"
                },
                {
                    "id": 14,
                    "parent_id": 12,
                    "title": "权限",
                    "path": "/system/authority",
                    "disabled": 0,
                    "hidden": 0,
                    "order": 1,
                    "menu_type": null,
                    "perms": null,
                    "create_datetime": "2025-03-18T13:48:45",
                    "update_datetime": "2025-03-18T13:48:45"
                }
            ]
        }
    ] as RouteConfig;

// 数据分析 类型的 历史对话数据
const chat_history = {
    "success": true,
    "err_code": null,
    "err_msg": null,
    "err_msg_zh": null,
    "data": [
        {
            "role": "human",
            "context": "分析2025年4月8日至2025年4月16日的航班旅客、货运量，并选择合适的图表生成",
            "order": 1,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "ai",
            "context": "\n\n{\n    \"thoughts\": \"用户需要分析2025年4月8日至2025年4月16日的航班旅客数量和货运量，并生成合适的图表。根据表结构，dm_opt_flight_psgcargo_stat_d表包含旅客数量(psgnum)和货邮重量(cargoweight)字段，且有航班日期(flightdate)可用于时间筛选。选择折线图展示时间序列数据趋势较为合适。\",\n    \"direct_response\": \"\",\n    \"sql\": \"SELECT flightdate::date AS date, SUM(psgnum) AS total_passengers, SUM(cargoweight) AS total_cargo_weight FROM dm_opt_flight_psgcargo_stat_d WHERE flightdate >= '2025-04-08' AND flightdate < '2025-04-17' GROUP BY flightdate::date ORDER BY flightdate::date LIMIT 50;\",\n    \"display_type\": \"response_line_chart\"\n}",
            "order": 1,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "view",
            "context": "```vis-thinking\\n\\n好的，我现在需要处理用户的问题：分析2025年4月8日至2025年4月16日的航班旅客、货运量，并选择合适的图表生成。首先，我需要理解用户的需求。用户想要在这段时间内了解航班的旅客数量和货运量，并且希望用合适的图表来展示这些数据。\\n\\n接下来，我需要查看提供的数据库表结构，确定哪些表和字段可以用来获取所需的数据。用户提供的数据库是xmn_dm，包含三个表：dm_opt_flight_psgcargo_stat_d、dwd_opt_fx_flightschedule和dwd_opt_flight_dynamic。根据表注释和字段定义，我需要找到与旅客数量和货运量相关的字段。\\n\\n首先看dm_opt_flight_psgcargo_stat_d表，这个表的注释提到有旅客数量（psgnum）和货邮重量（cargoweight）。这两个字段正好对应用户需要的旅客和货运量数据。此外，该表还有flightdate字段，可以用来筛选日期范围。因此，这个表可能是主要的数据来源。\\n\\n接下来确认日期范围。用户需要的是2025年4月8日至2025年4月16日的数据，所以需要使用flightdate字段进行过滤。另外，可能需要按天进行分组，以展示每天的数据变化趋势。\\n\\n然后考虑其他表是否有必要。dwd_opt_fx_flightschedule和dwd_opt_flight_dynamic表虽然也包含航班信息，但用户的问题主要关注统计结果，而dm_opt_flight_psgcargo_stat_d表似乎已经汇总了所需的psgnum和cargoweight，因此可能不需要连接其他表。\\n\\n接下来，我需要构建SQL查询。需要选择flightdate、psgnum和cargoweight，并按天分组。同时，过滤日期范围。需要注意的是，cargoweight的单位是千克，用户可能需要转换为吨，但问题中没有明确说明，所以暂时保持原单位。另外，可能需要使用SUM函数对psgnum和cargoweight进行汇总，按天分组。\\n\\n然后考虑图表类型。用户需要分析旅客和货运量的变化趋势，因此折线图（response_line_chart）比较合适，可以显示时间序列上的趋势。或者面积图（response_area_chart）也可以，但折线图更直接比较两个变量的变化。\\n\\n最后，验证SQL的正确性。确保表名、字段名正确，日期格式匹配，并且分组正确。同时，检查是否需要处理可能的NULL值或数据缺失的情况，但根据问题描述，暂时假设数据完整。\\n\\n总结，生成的SQL将从dm_opt_flight_psgcargo_stat_d表中提取指定日期范围内的数据，按天分组，汇总旅客和货运量，然后使用折线图展示趋势。\\n\\n```\\n用户需要分析2025年4月8日至2025年4月16日的航班旅客数量和货运量，并生成合适的图表。根据表结构，dm_opt_flight_psgcargo_stat_d表包含旅客数量(psgnum)和货邮重量(cargoweight)字段，且有航班日期(flightdate)可用于时间筛选。选择折线图展示时间序列数据趋势较为合适。\\n<chart-view content=\"{&quot;type&quot;: &quot;response_line_chart&quot;, &quot;sql&quot;: &quot;SELECT flightdate::date AS date, SUM(psgnum) AS total_passengers, SUM(cargoweight) AS total_cargo_weight FROM dm_opt_flight_psgcargo_stat_d WHERE flightdate &gt;= '2025-04-08' AND flightdate &lt; '2025-04-17' GROUP BY flightdate::date ORDER BY flightdate::date LIMIT 50;&quot;, &quot;data&quot;: [{&quot;date&quot;: &quot;2025-04-08T00:00:00&quot;, &quot;total_passengers&quot;: 1479.0, &quot;total_cargo_weight&quot;: 64548.263}, {&quot;date&quot;: &quot;2025-04-09T00:00:00&quot;, &quot;total_passengers&quot;: 1234.0, &quot;total_cargo_weight&quot;: 60323.692}, {&quot;date&quot;: &quot;2025-04-10T00:00:00&quot;, &quot;total_passengers&quot;: 1975.0, &quot;total_cargo_weight&quot;: 113547.489}, {&quot;date&quot;: &quot;2025-04-11T00:00:00&quot;, &quot;total_passengers&quot;: 932.0, &quot;total_cargo_weight&quot;: 22146.099}, {&quot;date&quot;: &quot;2025-04-12T00:00:00&quot;, &quot;total_passengers&quot;: 2245.0, &quot;total_cargo_weight&quot;: 78757.64}, {&quot;date&quot;: &quot;2025-04-13T00:00:00&quot;, &quot;total_passengers&quot;: 651.0, &quot;total_cargo_weight&quot;: 46924.032}, {&quot;date&quot;: &quot;2025-04-14T00:00:00&quot;, &quot;total_passengers&quot;: 1550.0, &quot;total_cargo_weight&quot;: 69686.22}, {&quot;date&quot;: &quot;2025-04-15T00:00:00&quot;, &quot;total_passengers&quot;: 738.0, &quot;total_cargo_weight&quot;: 47670.264}, {&quot;date&quot;: &quot;2025-04-16T00:00:00&quot;, &quot;total_passengers&quot;: 1146.0, &quot;total_cargo_weight&quot;: 55057.284}]}\" />",
            "order": 1,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "human",
            "context": "查询MF802的旅客、货运数据",
            "order": 2,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "ai",
            "context": "\n\n{\n    \"thoughts\": \"用户需要查询MF802航班的旅客数量和货运数据。根据表结构，dm_opt_flight_psgcargo_stat_d表包含航班号(flightno)、旅客数量(psgnum)和货邮重量(cargoweight)字段，适合用于该查询。由于未指定时间范围，将默认返回最新50条记录。\",\n    \"direct_response\": \"\",\n    \"sql\": \"SELECT flightno, psgnum, cargoweight FROM dm_opt_flight_psgcargo_stat_d WHERE flightno = 'MF802' ORDER BY flightdate DESC LIMIT 50;\",\n    \"display_type\": \"response_table\"\n}",
            "order": 2,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "view",
            "context": "```vis-thinking\\n\\n好的，用户现在要查询MF802航班的旅客和货运数据。首先，我需要确定MF802对应的航班号在数据库中的存储方式。根据之前的表结构，dm_opt_flight_psgcargo_stat_d表中有flightno字段，类型是varchar(16)，例子是“801”、“1872”。所以MF802可能存储为“MF802”或者可能只有数字部分，但用户输入的是MF802，应该直接作为字符串匹配。\\n\\n接下来，需要确定哪些表包含所需的旅客和货运数据。之前用户分析时间段的查询用了dm_opt_flight_psgcargo_stat_d表，其中psgnum是旅客数量，cargoweight是货运重量。所以这次查询也应该用这个表。\\n\\n然后，检查表结构中的字段。该表有bdp_id、flightdate、flightno、psgnum、cargoweight等字段。用户需要的是MF802的旅客和货运数据，所以需要筛选flightno='MF802'，并选择psgnum和cargoweight。\\n\\n另外，是否需要考虑时间范围？用户这次没有指定时间，但可能需要最新的数据或者所有数据。根据约束条件，如果没有指定行数，默认限制50条结果。因此，SQL应该包括LIMIT 50。\\n\\n还需要确认是否有其他相关表，比如dwd_opt_flight_dynamic或dwd_opt_fx_flightschedule，但根据用户之前的查询，旅客和货运数据主要在dm_opt_flight_psgcargo_stat_d表中，所以优先使用这个表。\\n\\n最后，生成SQL语句，选择flightno为MF802的记录，显示旅客数和货运重量，并限制结果数量。同时，根据数据展示方法，用户之前用折线图，但这次可能更适合表格展示，因为可能数据量不大，且需要具体数值。所以display_type选response_table。\\n\\n```\\n用户需要查询MF802航班的旅客数量和货运数据。根据表结构，dm_opt_flight_psgcargo_stat_d表包含航班号(flightno)、旅客数量(psgnum)和货邮重量(cargoweight)字段，适合用于该查询。由于未指定时间范围，将默认返回最新50条记录。\\n<chart-view content=\"{&quot;type&quot;: &quot;response_table&quot;, &quot;sql&quot;: &quot;SELECT flightno, psgnum, cargoweight FROM dm_opt_flight_psgcargo_stat_d WHERE flightno = 'MF802' ORDER BY flightdate DESC LIMIT 50;&quot;, &quot;data&quot;: [{&quot;flightno&quot;: &quot;MF802&quot;, &quot;psgnum&quot;: 173, &quot;cargoweight&quot;: 12740.0}]}\" />",
            "order": 2,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "human",
            "context": "2025年7月份航班、旅客、货邮数据，生成可视化图表。",
            "order": 3,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "ai",
            "context": "\n\n{\n    \"thoughts\": \"用户需要获取2025年7月的航班、旅客和货邮数据并生成可视化图表。根据表结构，dm_opt_flight_psgcargo_stat_d表包含航班日期(flightdate)、旅客数量(psgnum)、货邮重量(cargoweight)和航班数量(flightcount)字段。使用折线图展示时间序列数据趋势较为合适。\",\n    \"direct_response\": \"\",\n    \"sql\": \"SELECT flightdate::date AS date, SUM(flightcount) AS flight_num, SUM(psgnum) AS passenger_num, SUM(cargoweight) AS cargo_weight FROM dm_opt_flight_psgcargo_stat_d WHERE flightdate >= '2025-07-01' AND flightdate < '2025-08-01' GROUP BY flightdate::date ORDER BY flightdate::date LIMIT 50;\",\n    \"display_type\": \"response_line_chart\"\n}",
            "order": 3,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "view",
            "context": "```vis-thinking\\n\\n好的，用户需要查询2025年7月份的航班、旅客和货邮数据，并生成可视化图表。首先，我需要确定使用哪个表。根据之前的表结构，dm_opt_flight_psgcargo_stat_d表包含了航班日期（flightdate）、旅客数量（psgnum）和货邮重量（cargoweight）这些字段，非常适合用来回答这个问题。\\n\\n接下来，时间范围是2025年7月，所以需要筛选flightdate在2025-07-01到2025-07-31之间的数据。考虑到用户可能需要按天汇总的数据，应该将flightdate转换为日期类型，并按天分组。同时，用户要求生成可视化图表，这里选择折线图（response_line_chart）比较合适，因为折线图适合展示时间序列数据的趋势，可以清晰地显示每天的航班数量、旅客和货运量的变化。\\n\\n然后，需要统计每天的总旅客数和总货邮重量，以及航班数量。航班数量可能需要用COUNT函数，但根据表结构中的flightcount字段注释，该字段已经是0或1，所以可以直接用SUM(flightcount)来统计航班总数。不过用户的问题中提到“航班数据”，可能需要统计航班数量，所以需要确认是否直接使用flightcount字段的总和。此外，表中的flightcount字段描述中提到“对航班数量汇总时，筛选值为1，累计求和”，所以应该使用SUM(flightcount)来得到总航班数。\\n\\n最后，按照日期分组，并按日期排序，确保数据按时间顺序排列。同时，限制结果最多50条，符合约束条件中的默认限制。综合以上分析，构造SQL查询，选择日期、总旅客数、总货邮重量和总航班数，并按天分组，生成折线图展示数据趋势。\\n\\n```\\n用户需要获取2025年7月的航班、旅客和货邮数据并生成可视化图表。根据表结构，dm_opt_flight_psgcargo_stat_d表包含航班日期(flightdate)、旅客数量(psgnum)、货邮重量(cargoweight)和航班数量(flightcount)字段。使用折线图展示时间序列数据趋势较为合适。\\n<chart-view content=\"{&quot;type&quot;: &quot;response_line_chart&quot;, &quot;sql&quot;: &quot;SELECT flightdate::date AS date, SUM(flightcount) AS flight_num, SUM(psgnum) AS passenger_num, SUM(cargoweight) AS cargo_weight FROM dm_opt_flight_psgcargo_stat_d WHERE flightdate &gt;= '2025-07-01' AND flightdate &lt; '2025-08-01' GROUP BY flightdate::date ORDER BY flightdate::date LIMIT 50;&quot;, &quot;data&quot;: [{&quot;date&quot;: &quot;2025-07-01T00:00:00&quot;, &quot;flight_num&quot;: 12.0, &quot;passenger_num&quot;: 1793.0, &quot;cargo_weight&quot;: 70637.449}, {&quot;date&quot;: &quot;2025-07-02T00:00:00&quot;, &quot;flight_num&quot;: 6.0, &quot;passenger_num&quot;: 701.0, &quot;cargo_weight&quot;: 35606.206}, {&quot;date&quot;: &quot;2025-07-03T00:00:00&quot;, &quot;flight_num&quot;: 18.0, &quot;passenger_num&quot;: 2426.0, &quot;cargo_weight&quot;: 127032.901}, {&quot;date&quot;: &quot;2025-07-04T00:00:00&quot;, &quot;flight_num&quot;: 16.0, &quot;passenger_num&quot;: 2343.0, &quot;cargo_weight&quot;: 75166.452}, {&quot;date&quot;: &quot;2025-07-05T00:00:00&quot;, &quot;flight_num&quot;: 10.0, &quot;passenger_num&quot;: 1501.0, &quot;cargo_weight&quot;: 62534.501}, {&quot;date&quot;: &quot;2025-07-06T00:00:00&quot;, &quot;flight_num&quot;: 16.0, &quot;passenger_num&quot;: 2366.0, &quot;cargo_weight&quot;: 102592.046}, {&quot;date&quot;: &quot;2025-07-07T00:00:00&quot;, &quot;flight_num&quot;: 14.0, &quot;passenger_num&quot;: 1923.0, &quot;cargo_weight&quot;: 112052.908}, {&quot;date&quot;: &quot;2025-07-08T00:00:00&quot;, &quot;flight_num&quot;: 12.0, &quot;passenger_num&quot;: 1738.0, &quot;cargo_weight&quot;: 46317.061}, {&quot;date&quot;: &quot;2025-07-09T00:00:00&quot;, &quot;flight_num&quot;: 14.0, &quot;passenger_num&quot;: 1963.0, &quot;cargo_weight&quot;: 108492.393}, {&quot;date&quot;: &quot;2025-07-10T00:00:00&quot;, &quot;flight_num&quot;: 21.0, &quot;passenger_num&quot;: 2745.0, &quot;cargo_weight&quot;: 138885.921}, {&quot;date&quot;: &quot;2025-07-11T00:00:00&quot;, &quot;flight_num&quot;: 11.0, &quot;passenger_num&quot;: 1440.0, &quot;cargo_weight&quot;: 37253.387}]}\" />",
            "order": 3,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "human",
            "context": "你好",
            "order": 4,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "ai",
            "context": "\n\n{\n    \"thoughts\": \"用户发送了问候语'你好'，需要友好回应并引导其提出具体问题。\",\n    \"direct_response\": \"您好！有什么可以帮助您的吗？\",\n    \"sql\": \"\",\n    \"display_type\": \"\"\n}",
            "order": 4,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "view",
            "context": "```vis-thinking\\n\\n好的，用户这次又发了一个“你好”，看起来可能是测试或者开始对话。之前的历史对话中，用户已经询问过几次关于航班数据的查询，比如特定日期范围的旅客和货运量分析，以及特定航班号的查询。现在用户只是发送“你好”，没有具体的问题，所以需要判断如何回应。\\n\\n根据约束条件，如果上下文足够回答用户，可以直接回复而不需要SQL。这里用户没有提出具体问题，所以应该用友好的问候回应，并提示用户提出具体问题。同时，按照JSON格式的要求，需要包含thoughts、direct_response、sql和display_type字段。由于不需要执行SQL，direct_response应该包含问候语，而sql留空。显示类型也无需设置，因为没有数据需要展示。\\n\\n需要确保返回的JSON正确，没有语法错误，并且符合Python的json.loads解析要求。因此，正确的处理方式是在direct_response中回复“您好！有什么可以帮助您的吗？”，其他字段留空或设置为默认值。\\n\\n```\\n您好！有什么可以帮助您的吗？\\n",
            "order": 4,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "human",
            "context": "分析2025年4月的航班旅客、货运量，并选择合适的图表生成",
            "order": 5,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "ai",
            "context": "\n\n{\n    \"thoughts\": \"用户需要分析2025年4月的航班旅客数量和货运量，并生成合适的图表。根据表结构，dm_opt_flight_psgcargo_stat_d表包含旅客数量(psgnum)和货邮重量(cargoweight)字段，且有航班日期(flightdate)可用于时间筛选。选择折线图展示时间序列数据趋势较为合适。\",\n    \"direct_response\": \"\",\n    \"sql\": \"SELECT flightdate::date AS date, SUM(psgnum) AS total_passengers, SUM(cargoweight) AS total_cargo_weight FROM dm_opt_flight_psgcargo_stat_d WHERE flightdate >= '2025-04-01' AND flightdate < '2025-05-01' GROUP BY flightdate::date ORDER BY flightdate::date LIMIT 50;\",\n    \"display_type\": \"response_line_chart\"\n}",
            "order": 5,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "view",
            "context": "```vis-thinking\\n\\n好的，用户需要分析2025年4月的航班旅客和货运量，并生成合适的图表。首先，我需要确定使用哪个表。根据之前的表结构，dm_opt_flight_psgcargo_stat_d表中有psgnum和cargoweight字段，分别记录旅客数量和货邮重量，还有flightdate用于时间筛选。所以这个表是合适的数据源。\\n\\n接下来，用户要求分析整个4月份的数据，所以需要筛选flightdate在2025-04-01到2025-05-01之间的记录。按天分组可以观察每天的变化趋势，使用SUM函数汇总旅客和货运量。考虑到时间序列数据，折线图（response_line_chart）能有效展示趋势变化。同时，用户可能希望看到每天的具体数值，但根据之前的例子，用户可能更倾向于图表展示，所以选择折线图。\\n\\n需要确认SQL语句是否正确，特别是日期范围和字段分组。确保使用flightdate::date来按天分组，并计算总和。最后限制结果为50条，符合约束条件。没有其他表涉及，不需要关联其他表，所以SQL应该正确无误。\\n\\n```\\n用户需要分析2025年4月的航班旅客数量和货运量，并生成合适的图表。根据表结构，dm_opt_flight_psgcargo_stat_d表包含旅客数量(psgnum)和货邮重量(cargoweight)字段，且有航班日期(flightdate)可用于时间筛选。选择折线图展示时间序列数据趋势较为合适。\\n<chart-view content=\"{&quot;type&quot;: &quot;response_line_chart&quot;, &quot;sql&quot;: &quot;SELECT flightdate::date AS date, SUM(psgnum) AS total_passengers, SUM(cargoweight) AS total_cargo_weight FROM dm_opt_flight_psgcargo_stat_d WHERE flightdate &gt;= '2025-04-01' AND flightdate &lt; '2025-05-01' GROUP BY flightdate::date ORDER BY flightdate::date LIMIT 50;&quot;, &quot;data&quot;: [{&quot;date&quot;: &quot;2025-04-01T00:00:00&quot;, &quot;total_passengers&quot;: 1278.0, &quot;total_cargo_weight&quot;: 50436.53}, {&quot;date&quot;: &quot;2025-04-02T00:00:00&quot;, &quot;total_passengers&quot;: 1383.0, &quot;total_cargo_weight&quot;: 65652.548}, {&quot;date&quot;: &quot;2025-04-03T00:00:00&quot;, &quot;total_passengers&quot;: 1670.0, &quot;total_cargo_weight&quot;: 52420.226}, {&quot;date&quot;: &quot;2025-04-04T00:00:00&quot;, &quot;total_passengers&quot;: 1046.0, &quot;total_cargo_weight&quot;: 39231.585}, {&quot;date&quot;: &quot;2025-04-05T00:00:00&quot;, &quot;total_passengers&quot;: 1084.0, &quot;total_cargo_weight&quot;: 47607.38}, {&quot;date&quot;: &quot;2025-04-06T00:00:00&quot;, &quot;total_passengers&quot;: 721.0, &quot;total_cargo_weight&quot;: 38759.978}, {&quot;date&quot;: &quot;2025-04-07T00:00:00&quot;, &quot;total_passengers&quot;: 823.0, &quot;total_cargo_weight&quot;: 33279.089}, {&quot;date&quot;: &quot;2025-04-08T00:00:00&quot;, &quot;total_passengers&quot;: 1479.0, &quot;total_cargo_weight&quot;: 64548.263}, {&quot;date&quot;: &quot;2025-04-09T00:00:00&quot;, &quot;total_passengers&quot;: 1234.0, &quot;total_cargo_weight&quot;: 60323.692}, {&quot;date&quot;: &quot;2025-04-10T00:00:00&quot;, &quot;total_passengers&quot;: 1975.0, &quot;total_cargo_weight&quot;: 113547.489}, {&quot;date&quot;: &quot;2025-04-11T00:00:00&quot;, &quot;total_passengers&quot;: 932.0, &quot;total_cargo_weight&quot;: 22146.099}, {&quot;date&quot;: &quot;2025-04-12T00:00:00&quot;, &quot;total_passengers&quot;: 2245.0, &quot;total_cargo_weight&quot;: 78757.64}, {&quot;date&quot;: &quot;2025-04-13T00:00:00&quot;, &quot;total_passengers&quot;: 651.0, &quot;total_cargo_weight&quot;: 46924.032}, {&quot;date&quot;: &quot;2025-04-14T00:00:00&quot;, &quot;total_passengers&quot;: 1550.0, &quot;total_cargo_weight&quot;: 69686.22}, {&quot;date&quot;: &quot;2025-04-15T00:00:00&quot;, &quot;total_passengers&quot;: 738.0, &quot;total_cargo_weight&quot;: 47670.264}, {&quot;date&quot;: &quot;2025-04-16T00:00:00&quot;, &quot;total_passengers&quot;: 1146.0, &quot;total_cargo_weight&quot;: 55057.284}, {&quot;date&quot;: &quot;2025-04-17T00:00:00&quot;, &quot;total_passengers&quot;: 856.0, &quot;total_cargo_weight&quot;: 27192.436}, {&quot;date&quot;: &quot;2025-04-18T00:00:00&quot;, &quot;total_passengers&quot;: 1035.0, &quot;total_cargo_weight&quot;: 36017.671}, {&quot;date&quot;: &quot;2025-04-19T00:00:00&quot;, &quot;total_passengers&quot;: 1255.0, &quot;total_cargo_weight&quot;: 43567.62}, {&quot;date&quot;: &quot;2025-04-20T00:00:00&quot;, &quot;total_passengers&quot;: 1419.0, &quot;total_cargo_weight&quot;: 72364.406}, {&quot;date&quot;: &quot;2025-04-21T00:00:00&quot;, &quot;total_passengers&quot;: 1258.0, &quot;total_cargo_weight&quot;: 60472.114}, {&quot;date&quot;: &quot;2025-04-22T00:00:00&quot;, &quot;total_passengers&quot;: 1293.0, &quot;total_cargo_weight&quot;: 53656.037}, {&quot;date&quot;: &quot;2025-04-23T00:00:00&quot;, &quot;total_passengers&quot;: 800.0, &quot;total_cargo_weight&quot;: 35021.456}, {&quot;date&quot;: &quot;2025-04-24T00:00:00&quot;, &quot;total_passengers&quot;: 1245.0, &quot;total_cargo_weight&quot;: 25196.018}, {&quot;date&quot;: &quot;2025-04-25T00:00:00&quot;, &quot;total_passengers&quot;: 995.0, &quot;total_cargo_weight&quot;: 32305.788}, {&quot;date&quot;: &quot;2025-04-26T00:00:00&quot;, &quot;total_passengers&quot;: 1905.0, &quot;total_cargo_weight&quot;: 95635.519}, {&quot;date&quot;: &quot;2025-04-27T00:00:00&quot;, &quot;total_passengers&quot;: 1016.0, &quot;total_cargo_weight&quot;: 54243.295}, {&quot;date&quot;: &quot;2025-04-28T00:00:00&quot;, &quot;total_passengers&quot;: 1443.0, &quot;total_cargo_weight&quot;: 62044.91}, {&quot;date&quot;: &quot;2025-04-29T00:00:00&quot;, &quot;total_passengers&quot;: 1354.0, &quot;total_cargo_weight&quot;: 62139.488}, {&quot;date&quot;: &quot;2025-04-30T00:00:00&quot;, &quot;total_passengers&quot;: 1100.0, &quot;total_cargo_weight&quot;: 40614.784}]}\" />",
            "order": 5,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "human",
            "context": "分析2025年4月8日至2025年4月16日的航班旅客、货运量，并选择合适的图表生成",
            "order": 6,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "ai",
            "context": "\n\n{\n    \"thoughts\": \"用户需要分析2025年4月8日至2025年4月16日的航班旅客数量和货运量，并生成合适的图表。根据表结构，dm_opt_flight_psgcargo_stat_d表包含旅客数量(psgnum)和货邮重量(cargoweight)字段，且有航班日期(flightdate)可用于时间筛选。选择折线图展示时间序列数据趋势较为合适。\",\n    \"direct_response\": \"\",\n    \"sql\": \"SELECT flightdate::date AS date, SUM(psgnum) AS total_passengers, SUM(cargoweight) AS total_cargo_weight FROM dm_opt_flight_psgcargo_stat_d WHERE flightdate >= '2025-04-08' AND flightdate < '2025-04-17' GROUP BY flightdate::date ORDER BY flightdate::date LIMIT 50;\",\n    \"display_type\": \"response_line_chart\"\n}",
            "order": 6,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "view",
            "context": "```vis-thinking\\n\\n好的，用户再次提出了与之前类似的问题，需要分析2025年4月8日至2025年4月16日的航班旅客和货运量，并生成合适的图表。首先，我需要确认用户的需求是否与之前的查询有变化。之前的对话中，用户已经询问过类似时间段的数据，比如4月8日至16日，以及7月份的数据，还有特定航班MF802的信息。这次用户重复了同样的时间段，可能是在测试或者需要更详细的分析。\\n\\n接下来，检查可用的数据库表结构。用户提供的数据库是xmn_dm，其中dm_opt_flight_psgcargo_stat_d表包含了flightdate、psgnum、cargoweight等字段，这些正好符合用户的需求。需要确保时间范围的正确性，即从2025-04-08到2025-04-16，注意结束日期是否包含在内。通常，使用flightdate >= '2025-04-08' AND flightdate < '2025-04-17'来覆盖整个时间段。\\n\\n然后，考虑如何聚合数据。用户需要每天的旅客总数和货运量，所以需要按天分组，并使用SUM函数汇总psgnum和cargoweight。同时，按照之前的惯例，限制结果为50条记录，但用户可能只需要9天的数据，所以LIMIT 50不会有问题。\\n\\n关于图表选择，用户之前使用过折线图（response_line_chart），这适合展示时间序列的趋势，因此继续使用折线图是合适的。需要确保SQL查询中的日期格式正确，使用flightdate::date来转换时间戳为日期，以便按天分组。\\n\\n最后，确认是否有其他可能的表需要关联，但根据用户提供的表结构，当前表已经包含所需的所有字段，不需要连接其他表。因此，生成的SQL应该是正确的，并且能够满足用户的需求。\\n\\n```\\n用户需要分析2025年4月8日至2025年4月16日的航班旅客数量和货运量，并生成合适的图表。根据表结构，dm_opt_flight_psgcargo_stat_d表包含旅客数量(psgnum)和货邮重量(cargoweight)字段，且有航班日期(flightdate)可用于时间筛选。选择折线图展示时间序列数据趋势较为合适。\\n<chart-view content=\"{&quot;type&quot;: &quot;response_line_chart&quot;, &quot;sql&quot;: &quot;SELECT flightdate::date AS date, SUM(psgnum) AS total_passengers, SUM(cargoweight) AS total_cargo_weight FROM dm_opt_flight_psgcargo_stat_d WHERE flightdate &gt;= '2025-04-08' AND flightdate &lt; '2025-04-17' GROUP BY flightdate::date ORDER BY flightdate::date LIMIT 50;&quot;, &quot;data&quot;: [{&quot;date&quot;: &quot;2025-04-08T00:00:00&quot;, &quot;total_passengers&quot;: 1479.0, &quot;total_cargo_weight&quot;: 64548.263}, {&quot;date&quot;: &quot;2025-04-09T00:00:00&quot;, &quot;total_passengers&quot;: 1234.0, &quot;total_cargo_weight&quot;: 60323.692}, {&quot;date&quot;: &quot;2025-04-10T00:00:00&quot;, &quot;total_passengers&quot;: 1975.0, &quot;total_cargo_weight&quot;: 113547.489}, {&quot;date&quot;: &quot;2025-04-11T00:00:00&quot;, &quot;total_passengers&quot;: 932.0, &quot;total_cargo_weight&quot;: 22146.099}, {&quot;date&quot;: &quot;2025-04-12T00:00:00&quot;, &quot;total_passengers&quot;: 2245.0, &quot;total_cargo_weight&quot;: 78757.64}, {&quot;date&quot;: &quot;2025-04-13T00:00:00&quot;, &quot;total_passengers&quot;: 651.0, &quot;total_cargo_weight&quot;: 46924.032}, {&quot;date&quot;: &quot;2025-04-14T00:00:00&quot;, &quot;total_passengers&quot;: 1550.0, &quot;total_cargo_weight&quot;: 69686.22}, {&quot;date&quot;: &quot;2025-04-15T00:00:00&quot;, &quot;total_passengers&quot;: 738.0, &quot;total_cargo_weight&quot;: 47670.264}, {&quot;date&quot;: &quot;2025-04-16T00:00:00&quot;, &quot;total_passengers&quot;: 1146.0, &quot;total_cargo_weight&quot;: 55057.284}]}\" />",
            "order": 6,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "human",
            "context": "查询2025年5月厦航进港航班列表，并统计总量。",
            "order": 7,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "ai",
            "context": "\n\n{\n    \"thoughts\": \"用户需要查询2025年5月厦航进港航班列表并统计总量。根据表结构，dm_opt_flight_psgcargo_stat_d表包含航班ID(bdp_id)、航司代码(airlineiatacode)、进出港方向(direction)、航班日期(flightdate)、旅客数量(psgnum)和货邮重量(cargoweight)字段，适合用于该查询。厦航的IATA代码为MF，需筛选direction='进港'。\",\n    \"direct_response\": \"\",\n    \"sql\": \"SELECT bdp_id AS 航班ID, flightno AS 航班号, flightdate AS 航班日期, psgnum AS 旅客数量, cargoweight AS 货邮重量 FROM dm_opt_flight_psgcargo_stat_d WHERE airlineiatacode = 'MF' AND direction = '进港' AND flightdate >= '2025-05-01' AND flightdate < '2025-06-01' ORDER BY flightdate DESC LIMIT 50;\",\n    \"display_type\": \"response_table\"\n}",
            "order": 7,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "view",
            "context": "```vis-thinking\\n\\n好的，用户需要查询2025年5月厦航进港航班列表并统计总量。首先，我需要确定使用哪个表。根据提供的表结构，dm_opt_flight_psgcargo_stat_d表中有airlineiatacode（航司二字码）和direction（进出港方向）字段，适合用来筛选厦航进港的航班。\\n\\n接下来，用户提到的“厦航”应该对应airlineiatacode为'MF'，因为厦航的IATA代码是MF。然后，direction字段需要是进港，根据定义，direction的值可能是“进港”或者对应的英文，但表结构中显示direction是VARCHAR(16)，所以需要确认具体值。但用户之前的问题中可能使用的是中文，所以这里假设direction为'进港'。\\n\\n时间范围是2025年5月，所以flightdate需要在这个月份内。统计总量的话，需要计算航班数量、旅客总数和货运总量。航班数量可能对应flightcount字段，但用户可能需要统计记录数，所以可能需要COUNT(*)。不过表中已有flightcount字段，可能每个记录代表一个航班，所以SUM(flightcount)可能更准确，但根据之前的例子，用户可能直接使用COUNT(*)来统计航班数量。\\n\\n然后，需要列出航班列表，可能需要选择bdp_id作为航班ID，flightno作为航班号，flightdate等信息。统计总量时，使用SUM(psgnum)和SUM(cargoweight)来汇总旅客和货运量。\\n\\n最后，生成SQL时需要分组，但用户需要列表和统计总量，可能需要两个查询。但用户可能希望在一个查询中同时获取列表和统计，但通常列表和统计是分开的。不过用户可能希望先列出航班，然后统计总量，所以可能需要两个部分。但根据用户的问题，可能只需要一个查询来获取列表，并单独统计总量。但根据之前的回答，用户可能希望在一个查询中统计总量，而列表可能需要另一个查询。但用户的问题可能希望同时得到列表和总量统计，所以可能需要两个查询，但根据约束条件，只能生成一个SQL。因此，可能需要优先统计总量，或者生成两个查询。但根据用户的问题描述，可能只需要统计总量，而列表可能只是展示，但用户明确说“并统计总量”，所以可能需要统计总量，而列表可能作为详细数据。\\n\\n因此，最终的SQL可能分为两部分：一部分列出航班，另一部分统计总量。但根据用户的问题，可能只需要统计总量，所以生成一个汇总的SQL，使用SUM函数，并按天分组，或者整体统计。但用户可能需要总量，所以不需要按天分组，直接SUM即可。同时，需要确保筛选条件正确，包括airlineiatacode='MF'，direction='进港'，flightdate在2025-05-01到2025-05-31之间。\\n\\n检查表结构中的字段，dm_opt_flight_psgcargo_stat_d表中的flightdate是timestamp类型，所以需要用BETWEEN或者>=和<=来筛选日期范围。同时，LIMIT 50来限制结果数量。\\n\\n最终的SQL应该是选择SUM(flightcount)、SUM(psgnum)、SUM(cargoweight)，并应用WHERE条件过滤航司、方向和日期。同时，可能需要显示航班列表，但用户可能更关注统计总量，所以生成统计总量的SQL，并使用表格展示结果。\\n\\n```\\n用户需要查询2025年5月厦航进港航班列表并统计总量。根据表结构，dm_opt_flight_psgcargo_stat_d表包含航班ID(bdp_id)、航司代码(airlineiatacode)、进出港方向(direction)、航班日期(flightdate)、旅客数量(psgnum)和货邮重量(cargoweight)字段，适合用于该查询。厦航的IATA代码为MF，需筛选direction='进港'。\\n<chart-view content=\"{&quot;type&quot;: &quot;response_table&quot;, &quot;sql&quot;: &quot;SELECT bdp_id AS 航班ID, flightno AS 航班号, flightdate AS 航班日期, psgnum AS 旅客数量, cargoweight AS 货邮重量 FROM dm_opt_flight_psgcargo_stat_d WHERE airlineiatacode = 'MF' AND direction = '进港' AND flightdate &gt;= '2025-05-01' AND flightdate &lt; '2025-06-01' ORDER BY flightdate DESC LIMIT 50;&quot;, &quot;data&quot;: [{&quot;航班id&quot;: &quot;MF7877-20250530&quot;, &quot;航班号&quot;: &quot;MF7877&quot;, &quot;航班日期&quot;: &quot;2025-05-30T12:13:00&quot;, &quot;旅客数量&quot;: 100, &quot;货邮重量&quot;: 4596.723}, {&quot;航班id&quot;: &quot;MF6994-20250529&quot;, &quot;航班号&quot;: &quot;MF6994&quot;, &quot;航班日期&quot;: &quot;2025-05-29T14:09:00&quot;, &quot;旅客数量&quot;: 158, &quot;货邮重量&quot;: 325.29}, {&quot;航班id&quot;: &quot;MF6059-20250528&quot;, &quot;航班号&quot;: &quot;MF6059&quot;, &quot;航班日期&quot;: &quot;2025-05-28T15:02:00&quot;, &quot;旅客数量&quot;: 167, &quot;货邮重量&quot;: 13715.736}, {&quot;航班id&quot;: &quot;MF175-20250527&quot;, &quot;航班号&quot;: &quot;MF175&quot;, &quot;航班日期&quot;: &quot;2025-05-27T09:31:00&quot;, &quot;旅客数量&quot;: 100, &quot;货邮重量&quot;: 326.18}, {&quot;航班id&quot;: &quot;MF1901-20250526&quot;, &quot;航班号&quot;: &quot;MF1901&quot;, &quot;航班日期&quot;: &quot;2025-05-26T19:11:00&quot;, &quot;旅客数量&quot;: 163, &quot;货邮重量&quot;: 4433.096}, {&quot;航班id&quot;: &quot;MF5510-20250521&quot;, &quot;航班号&quot;: &quot;MF5510&quot;, &quot;航班日期&quot;: &quot;2025-05-21T10:24:00&quot;, &quot;旅客数量&quot;: 105, &quot;货邮重量&quot;: 12144.315}, {&quot;航班id&quot;: &quot;MF410-20250521&quot;, &quot;航班号&quot;: &quot;MF410&quot;, &quot;航班日期&quot;: &quot;2025-05-21T09:15:00&quot;, &quot;旅客数量&quot;: 98, &quot;货邮重量&quot;: 5038.802}, {&quot;航班id&quot;: &quot;MF9393-20250521&quot;, &quot;航班号&quot;: &quot;MF9393&quot;, &quot;航班日期&quot;: &quot;2025-05-21T08:28:00&quot;, &quot;旅客数量&quot;: 176, &quot;货邮重量&quot;: 11683.648}, {&quot;航班id&quot;: &quot;MF9551-20250520&quot;, &quot;航班号&quot;: &quot;MF9551&quot;, &quot;航班日期&quot;: &quot;2025-05-20T17:37:00&quot;, &quot;旅客数量&quot;: 182, &quot;货邮重量&quot;: 4955.523}, {&quot;航班id&quot;: &quot;MF8449-20250520&quot;, &quot;航班号&quot;: &quot;MF8449&quot;, &quot;航班日期&quot;: &quot;2025-05-20T11:58:00&quot;, &quot;旅客数量&quot;: 90, &quot;货邮重量&quot;: 4695.13}, {&quot;航班id&quot;: &quot;MF9407-20250514&quot;, &quot;航班号&quot;: &quot;MF9407&quot;, &quot;航班日期&quot;: &quot;2025-05-14T11:52:00&quot;, &quot;旅客数量&quot;: 165, &quot;货邮重量&quot;: 4722.601}, {&quot;航班id&quot;: &quot;MF7448-20250513&quot;, &quot;航班号&quot;: &quot;MF7448&quot;, &quot;航班日期&quot;: &quot;2025-05-13T20:59:00&quot;, &quot;旅客数量&quot;: 99, &quot;货邮重量&quot;: 307.045}, {&quot;航班id&quot;: &quot;MF1028-20250508&quot;, &quot;航班号&quot;: &quot;MF1028&quot;, &quot;航班日期&quot;: &quot;2025-05-08T18:35:00&quot;, &quot;旅客数量&quot;: 177, &quot;货邮重量&quot;: 313.979}]}\" />",
            "order": 7,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "human",
            "context": "增加总计",
            "order": 8,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "human",
            "context": "查询2025年5月厦航进港航班列表，并统计航班，旅客、货邮总数。",
            "order": 9,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "ai",
            "context": "\n\n{\n    \"thoughts\": \"用户需要查询2025年5月厦航进港航班列表并统计航班数量、旅客总数和货邮总数。根据表结构，dm_opt_flight_psgcargo_stat_d表包含航班号(flightno)、旅客数量(psgnum)、货邮重量(cargoweight)和方向(direction)字段。通过筛选airlineiatacode='MF'（厦航代码）、direction='进港'及时间范围，可获取所需数据。使用UNION ALL将列表与总计合并展示。\",\n    \"direct_response\": \"\",\n    \"sql\": \"SELECT flightno, flightdate, psgnum, cargoweight FROM dm_opt_flight_psgcargo_stat_d WHERE airlineiatacode = 'MF' AND direction = '进港' AND flightdate >= '2025-05-01' AND flightdate < '2025-06-01' UNION ALL SELECT '总计', NULL, SUM(psgnum), SUM(cargoweight) FROM dm_opt_flight_psgcargo_stat_d WHERE airlineiatacode = 'MF' AND direction = '进港' AND flightdate >= '2025-05-01' AND flightdate < '2025-06-01' ORDER BY flightdate DESC NULLS LAST LIMIT 51;\",\n    \"display_type\": \"response_table\"\n}",
            "order": 9,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "view",
            "context": "```vis-thinking\\n\\n好的，用户现在需要查询2025年5月厦航的进港航班列表，并统计航班数量、旅客总数和货邮总数。首先，我需要回顾之前的对话历史，看看用户之前的需求和我的回应。之前用户多次要求类似的分析，比如4月份的数据和特定日期范围的统计，我都是使用dm_opt_flight_psgcargo_stat_d表，并且用折线图展示趋势。但这次用户明确要求列出航班列表并统计总量，可能需要分两部分处理：列表和总计。\\n\\n首先，确定数据来源。根据提供的表结构，dm_opt_flight_psgcargo_stat_d表包含航班ID、航班号、航班日期、旅客数量、货邮重量等字段，同时有航司代码（airlineiatacode）和方向（direction）字段，适合筛选厦航（MF）的进港航班。时间范围是2025年5月，即flightdate在'2025-05-01'到'2025-05-31'之间。\\n\\n用户需要两个部分的结果：航班列表和总计。列表部分需要展示每个航班的详细信息，而总计则需要计算总航班数、旅客总数和货邮总重量。可能需要两个查询，或者使用子查询或窗口函数来同时获取。但考虑到性能优化，可能分开处理更高效。\\n\\n对于列表部分，查询条件包括airlineiatacode = 'MF'，direction = '进港'，flightdate在5月范围内。选择需要的字段如航班ID、航班号、日期、旅客数、货邮重量，并按日期降序排列，限制50条结果。\\n\\n总计部分需要统计总航班数（COUNT(flightno)），总旅客数（SUM(psgnum)），总货邮重量（SUM(cargoweight)）。同样应用相同的筛选条件。可能需要单独的查询，或者在列表查询中使用窗口函数，但分开可能更清晰。\\n\\n用户可能希望结果中同时显示列表和总计，但根据约束条件，每个查询只能生成一个SQL。因此可能需要两个不同的查询，或者在结果展示时合并。但根据用户当前的问题，可能希望在一个查询中获取列表和总计。不过PostgreSQL中可以通过子查询或UNION来实现，但可能复杂。考虑到用户可能只需要分开的列表和总计，可能需要两个SQL语句，但根据之前的例子，用户可能希望一个查询返回列表，另一个返回总计。但用户的问题可能希望在一个结果中展示列表和总计行，这可能需要使用UNION ALL添加总计行到列表末尾，但需要调整字段匹配。\\n\\n例如，使用UNION ALL将总计行添加到列表结果中，但需要确保字段数量和类型一致。例如，航班ID、航班号、日期等字段在总计行中可以为NULL或标记为总计。但这样可能需要复杂的查询结构。\\n\\n或者，用户可能只需要两个独立的查询：一个列出航班，另一个显示总计。根据之前的回复，用户可能希望先获取列表，然后单独获取总计。因此，可能需要生成两个SQL，但根据约束条件，每次只能生成一个SQL。因此可能需要优先满足列表查询，并在结果中附带总计，或者用户可能希望分开处理。\\n\\n根据用户当前的问题，可能希望在一个查询中获取列表和总计。因此，可能需要使用子查询来计算总计，并在主查询中列出航班，但这样可能无法在同一个结果集中展示。或者，使用窗口函数计算总计，但可能不太直观。\\n\\n综合考虑，可能生成两个SQL：一个用于列表，另一个用于总计。但根据用户的问题，可能希望在一个查询中完成。因此，可能需要使用UNION ALL来添加总计行，例如：\\n\\nSELECT flightno, flightdate, psgnum, cargoweight FROM ... \\nUNION ALL\\nSELECT '总计', NULL, SUM(psgnum), SUM(cargoweight) FROM ... \\n\\n但需要确保字段数量和类型匹配。例如，第一个查询选择flightno, flightdate, psgnum, cargoweight，第二个查询选择NULL或'总计'作为flightno，NULL作为flightdate，总和作为psgnum和cargoweight。但可能需要调整字段顺序和类型。\\n\\n不过，根据用户的问题，可能更倾向于先获取列表，然后单独统计总计。因此，可能生成两个SQL，但根据之前的例子，用户可能希望一个查询。因此，可能需要使用子查询或窗口函数。\\n\\n最终，考虑到用户可能需要分开的列表和总计，生成两个SQL，但根据约束条件，每次只能生成一个SQL。因此，可能优先生成列表查询，并在结果中附带总计，或者用户可能需要进一步操作。但根据用户的问题，可能希望一个查询返回列表和总计行，因此需要构造这样的查询。\\n\\n因此，最终的SQL可能如下：\\n\\nSELECT flightno, flightdate, psgnum, cargoweight\\nFROM dm_opt_flight_psgcargo_stat_d\\nWHERE airlineiatacode = 'MF' AND direction = '进港' AND flightdate >= '2025-05-01' AND flightdate < '2025-06-01'\\nUNION ALL\\nSELECT '总计' AS flightno, NULL::timestamp AS flightdate, SUM(psgnum) AS psgnum, SUM(cargoweight) AS cargoweight\\nFROM dm_opt_flight_psgcargo_stat_d\\nWHERE airlineiatacode = 'MF' AND direction = '进港' AND flightdate >= '2025-05-01' AND flightdate < '2025-06-01'\\nORDER BY flightdate DESC NULLS LAST;\\n\\n这样，结果集将包括所有符合条件的航班记录，以及最后一行的总计。但需要注意字段类型匹配，例如flightno在总计行中为字符串，而其他行是航班号，可能需要转换。此外，flightdate在总计行为NULL，排序时放在最后。这样可以满足用户的需求，同时符合PostgreSQL的语法。\\n\\n最后，选择表格作为显示方式，因为需要展示多个字段和总计行。\\n\\n```\\n用户需要查询2025年5月厦航进港航班列表并统计航班数量、旅客总数和货邮总数。根据表结构，dm_opt_flight_psgcargo_stat_d表包含航班号(flightno)、旅客数量(psgnum)、货邮重量(cargoweight)和方向(direction)字段。通过筛选airlineiatacode='MF'（厦航代码）、direction='进港'及时间范围，可获取所需数据。使用UNION ALL将列表与总计合并展示。\\n<chart-view content=\"{&quot;type&quot;: &quot;response_table&quot;, &quot;sql&quot;: &quot;SELECT flightno, flightdate, psgnum, cargoweight FROM dm_opt_flight_psgcargo_stat_d WHERE airlineiatacode = 'MF' AND direction = '进港' AND flightdate &gt;= '2025-05-01' AND flightdate &lt; '2025-06-01' UNION ALL SELECT '总计', NULL, SUM(psgnum), SUM(cargoweight) FROM dm_opt_flight_psgcargo_stat_d WHERE airlineiatacode = 'MF' AND direction = '进港' AND flightdate &gt;= '2025-05-01' AND flightdate &lt; '2025-06-01' ORDER BY flightdate DESC NULLS LAST LIMIT 51;&quot;, &quot;data&quot;: [{&quot;flightno&quot;: &quot;MF7877&quot;, &quot;flightdate&quot;: &quot;2025-05-30T12:13:00&quot;, &quot;psgnum&quot;: 100.0, &quot;cargoweight&quot;: 4596.723}, {&quot;flightno&quot;: &quot;MF6994&quot;, &quot;flightdate&quot;: &quot;2025-05-29T14:09:00&quot;, &quot;psgnum&quot;: 158.0, &quot;cargoweight&quot;: 325.29}, {&quot;flightno&quot;: &quot;MF6059&quot;, &quot;flightdate&quot;: &quot;2025-05-28T15:02:00&quot;, &quot;psgnum&quot;: 167.0, &quot;cargoweight&quot;: 13715.736}, {&quot;flightno&quot;: &quot;MF175&quot;, &quot;flightdate&quot;: &quot;2025-05-27T09:31:00&quot;, &quot;psgnum&quot;: 100.0, &quot;cargoweight&quot;: 326.18}, {&quot;flightno&quot;: &quot;MF1901&quot;, &quot;flightdate&quot;: &quot;2025-05-26T19:11:00&quot;, &quot;psgnum&quot;: 163.0, &quot;cargoweight&quot;: 4433.096}, {&quot;flightno&quot;: &quot;MF5510&quot;, &quot;flightdate&quot;: &quot;2025-05-21T10:24:00&quot;, &quot;psgnum&quot;: 105.0, &quot;cargoweight&quot;: 12144.315}, {&quot;flightno&quot;: &quot;MF410&quot;, &quot;flightdate&quot;: &quot;2025-05-21T09:15:00&quot;, &quot;psgnum&quot;: 98.0, &quot;cargoweight&quot;: 5038.802}, {&quot;flightno&quot;: &quot;MF9393&quot;, &quot;flightdate&quot;: &quot;2025-05-21T08:28:00&quot;, &quot;psgnum&quot;: 176.0, &quot;cargoweight&quot;: 11683.648}, {&quot;flightno&quot;: &quot;MF9551&quot;, &quot;flightdate&quot;: &quot;2025-05-20T17:37:00&quot;, &quot;psgnum&quot;: 182.0, &quot;cargoweight&quot;: 4955.523}, {&quot;flightno&quot;: &quot;MF8449&quot;, &quot;flightdate&quot;: &quot;2025-05-20T11:58:00&quot;, &quot;psgnum&quot;: 90.0, &quot;cargoweight&quot;: 4695.13}, {&quot;flightno&quot;: &quot;MF9407&quot;, &quot;flightdate&quot;: &quot;2025-05-14T11:52:00&quot;, &quot;psgnum&quot;: 165.0, &quot;cargoweight&quot;: 4722.601}, {&quot;flightno&quot;: &quot;MF7448&quot;, &quot;flightdate&quot;: &quot;2025-05-13T20:59:00&quot;, &quot;psgnum&quot;: 99.0, &quot;cargoweight&quot;: 307.045}, {&quot;flightno&quot;: &quot;MF1028&quot;, &quot;flightdate&quot;: &quot;2025-05-08T18:35:00&quot;, &quot;psgnum&quot;: 177.0, &quot;cargoweight&quot;: 313.979}, {&quot;flightno&quot;: &quot;总计&quot;, &quot;flightdate&quot;: null, &quot;psgnum&quot;: 1780.0, &quot;cargoweight&quot;: 67258.068}]}\" />",
            "order": 9,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        }
    ]
}

// 创建工作流 历史对话：模版是chat_agent 
const di_chat_agent = {
    "success": true,
    "err_code": null,
    "err_msg": null,
    "err_msg_zh": null,
    "data": [
        {
            "role": "human",
            "context": "创建离线同步任务，数据来源是SC，目标源是SC，数据来源表是ods_ceshi620，数据目标表是dwd_ceshi620，数据全量同步，创建工作流并上线",
            "order": 1,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "view",
            "context": "```agent-plans\n[{\"name\": \"创建离线同步任务，数据来源是SC，目标源是SC，数据来源表是ods_ceshi620，数据目标表是dwd_ceshi620，数据全量同步，创建工作流并上线\", \"num\": 2, \"status\": \"complete\", \"agent\": \"PlanManager\", \"markdown\": \"```agent-messages\\n[{\\\"sender\\\": \\\"Human\\\", \\\"receiver\\\": \\\"PlanManager\\\", \\\"model\\\": null, \\\"markdown\\\": \\\"创建离线同步任务，数据来源是SC，目标源是SC，数据来源表是ods_ceshi620，数据目标表是dwd_ceshi620，数据全量同步，创建工作流并上线\\\", \\\"resource\\\": null}, {\\\"sender\\\": \\\"PlanManager\\\", \\\"receiver\\\": \\\"Human\\\", \\\"model\\\": null, \\\"markdown\\\": \\\"```vis-plugin\\\\n{\\\\\\\"name\\\\\\\": \\\\\\\"doCreateDiProcess\\\\\\\", \\\\\\\"args\\\\\\\": {\\\\\\\"diTaskParams\\\\\\\": {\\\\\\\"processName\\\\\\\": \\\\\\\"ods_ceshi620_to_dwd_ceshi620\\\\\\\", \\\\\\\"name\\\\\\\": \\\\\\\"ods_ceshi620_to_dwd_ceshi620\\\\\\\", \\\\\\\"columnMapType\\\\\\\": \\\\\\\"0\\\\\\\", \\\\\\\"writeMode\\\\\\\": \\\\\\\"0\\\\\\\", \\\\\\\"sourceId\\\\\\\": \\\\\\\"1\\\\\\\", \\\\\\\"sinkId\\\\\\\": \\\\\\\"2\\\\\\\", \\\\\\\"sourceDatabase\\\\\\\": \\\\\\\"noah\\\\\\\", \\\\\\\"sinkDatabase\\\\\\\": \\\\\\\"noah\\\\\\\", \\\\\\\"sourceType\\\\\\\": \\\\\\\"5\\\\\\\", \\\\\\\"sinkType\\\\\\\": \\\\\\\"5\\\\\\\", \\\\\\\"sourceTable\\\\\\\": \\\\\\\"ods_ceshi620\\\\\\\", \\\\\\\"sinkTable\\\\\\\": \\\\\\\"dwd_ceshi620\\\\\\\"}}, \\\\\\\"status\\\\\\\": \\\\\\\"complete\\\\\\\", \\\\\\\"logo\\\\\\\": null, \\\\\\\"result\\\\\\\": \\\\\\\"meta=None content=[TextContent(type='text', text='Cannot invoke \\\\\\\\\\\\\\\"com.alibaba.fastjson2.JSONObject.getJSONArray(String)\\\\\\\\\\\\\\\" because the return value of \\\\\\\\\\\\\\\"com.alibaba.fastjson2.JSONObject.getJSONObject(String)\\\\\\\\\\\\\\\" is null', annotations=None)] isError=True\\\\\\\", \\\\\\\"err_msg\\\\\\\": null}\\\\n```\\\", \\\"resource\\\": null}]\\n```\"}]\n```\n```agent-messages\n[{\"sender\": \"Planner\", \"receiver\": \"PlanManager\", \"model\": \"deepseek-r1-distill-qwen\", \"markdown\": \"- 1.调用getDatasourceInfo API获取数据源SC的信息，包括数据源ID。[ToolExpert]\\n- 2.调用getColumnsByTable API查询ods_ceshi620表的字段信息。[ToolExpert]\\n- 3.调用getColumnsByTable API查询dwd_ceshi620表的字段信息。[ToolExpert]\\n- 4.根据获取的数据源信息和字段信息，生成集成任务配置内容，包括数据源ID、目标ID、表名和字段映射。[di_agent]\\n- 5.调用doCreateDiProcess API创建集成任务并运行工作流。[ToolExpert]\", \"resource\": null}]\n```\n```agent-plans\n[{\"name\": \"调用getDatasourceInfo API获取数据源SC的信息，包括数据源ID。\", \"num\": 2, \"status\": \"complete\", \"agent\": \"ToolExpert\", \"markdown\": \"```agent-messages\\n[{\\\"sender\\\": \\\"PlanManager\\\", \\\"receiver\\\": \\\"ToolExpert\\\", \\\"model\\\": null, \\\"markdown\\\": \\\"调用getDatasourceInfo API获取数据源SC的信息，包括数据源ID。\\\", \\\"resource\\\": null}, {\\\"sender\\\": \\\"ToolExpert\\\", \\\"receiver\\\": \\\"PlanManager\\\", \\\"model\\\": \\\"deepseek-r1-distill-qwen\\\", \\\"markdown\\\": \\\"```vis-plugin\\\\n{\\\\\\\"name\\\\\\\": \\\\\\\"getDatasourceInfo\\\\\\\", \\\\\\\"args\\\\\\\": {\\\\\\\"name\\\\\\\": \\\\\\\"SC\\\\\\\"}, \\\\\\\"status\\\\\\\": \\\\\\\"complete\\\\\\\", \\\\\\\"logo\\\\\\\": null, \\\\\\\"result\\\\\\\": \\\\\\\"meta=None content=[TextContent(type='text', text='\\\\\\\\\\\\\\\"[{\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"id\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":8,\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"userId\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":0,\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"name\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"SC\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\",\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"type\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":6,\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"externalDatasourceConfig\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"{\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"database\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"noah_dq\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\",\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"ipAddress\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"192.168.6.201\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\",\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"port\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"8123\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\",\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"user\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"admin\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\",\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"password\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"admin\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\",\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"other\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":{},\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"customerPro\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":{}}\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"}]\\\\\\\\\\\\\\\"', annotations=None)] isError=False\\\\\\\", \\\\\\\"err_msg\\\\\\\": null}\\\\n```\\\", \\\"resource\\\": null}]\\n```\"}, {\"name\": \"调用getColumnsByTable API查询ods_ceshi620表的字段信息。\", \"num\": 4, \"status\": \"complete\", \"agent\": \"ToolExpert\", \"markdown\": \"```agent-messages\\n[{\\\"sender\\\": \\\"PlanManager\\\", \\\"receiver\\\": \\\"ToolExpert\\\", \\\"model\\\": null, \\\"markdown\\\": \\\"Read the result data of the dependent steps in the above historical message to complete the current goal:调用getColumnsByTable API查询ods_ceshi620表的字段信息。\\\", \\\"resource\\\": null}, {\\\"sender\\\": \\\"ToolExpert\\\", \\\"receiver\\\": \\\"PlanManager\\\", \\\"model\\\": \\\"deepseek-r1-distill-qwen\\\", \\\"markdown\\\": \\\"```vis-plugin\\\\n{\\\\\\\"name\\\\\\\": \\\\\\\"getColumnsByTable\\\\\\\", \\\\\\\"args\\\\\\\": {\\\\\\\"id\\\\\\\": 8, \\\\\\\"table\\\\\\\": \\\\\\\"ods_ceshi620\\\\\\\"}, \\\\\\\"status\\\\\\\": \\\\\\\"complete\\\\\\\", \\\\\\\"logo\\\\\\\": null, \\\\\\\"result\\\\\\\": \\\\\\\"meta=None content=[TextContent(type='text', text='\\\\\\\\\\\\\\\"{\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"code\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":1,\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"message\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"操作成功\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\",\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"data\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":[{\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"columnName\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"customer_id\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\",\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"displayName\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"customer_id\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\",\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"dataType\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"String\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\",\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"columnComment\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"客户编码\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\",\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"isNullable\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"NO\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\",\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"columnFamily\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":null,\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"primaryKey\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":true},{\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"columnName\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"customer_name\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\",\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"displayName\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"customer_name\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\",\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"dataType\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"String\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\",\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"columnComment\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"客户名称\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\",\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"isNullable\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"YES\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\",\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"columnFamily\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":null,\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"primaryKey\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":false}]}\\\\\\\\\\\\\\\"', annotations=None)] isError=False\\\\\\\", \\\\\\\"err_msg\\\\\\\": null}\\\\n```\\\", \\\"resource\\\": null}]\\n```\"}, {\"name\": \"调用getColumnsByTable API查询dwd_ceshi620表的字段信息。\", \"num\": 6, \"status\": \"complete\", \"agent\": \"ToolExpert\", \"markdown\": \"```agent-messages\\n[{\\\"sender\\\": \\\"PlanManager\\\", \\\"receiver\\\": \\\"ToolExpert\\\", \\\"model\\\": null, \\\"markdown\\\": \\\"Read the result data of the dependent steps in the above historical message to complete the current goal:调用getColumnsByTable API查询dwd_ceshi620表的字段信息。\\\", \\\"resource\\\": null}, {\\\"sender\\\": \\\"ToolExpert\\\", \\\"receiver\\\": \\\"PlanManager\\\", \\\"model\\\": \\\"deepseek-r1-distill-qwen\\\", \\\"markdown\\\": \\\"```vis-plugin\\\\n{\\\\\\\"name\\\\\\\": \\\\\\\"getColumnsByTable\\\\\\\", \\\\\\\"args\\\\\\\": {\\\\\\\"id\\\\\\\": 8, \\\\\\\"table\\\\\\\": \\\\\\\"dwd_ceshi620\\\\\\\"}, \\\\\\\"status\\\\\\\": \\\\\\\"complete\\\\\\\", \\\\\\\"logo\\\\\\\": null, \\\\\\\"result\\\\\\\": \\\\\\\"meta=None content=[TextContent(type='text', text='\\\\\\\\\\\\\\\"{\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"code\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":1,\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"message\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"操作成功\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\",\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"data\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":[]}\\\\\\\\\\\\\\\"', annotations=None)] isError=False\\\\\\\", \\\\\\\"err_msg\\\\\\\": null}\\\\n```\\\", \\\"resource\\\": null}]\\n```\"}, {\"name\": \"根据获取的数据源信息和字段信息，生成集成任务配置内容，包括数据源ID、目标ID、表名和字段映射。\", \"num\": 8, \"status\": \"complete\", \"agent\": \"di_agent\", \"markdown\": \"```agent-messages\\n[{\\\"sender\\\": \\\"PlanManager\\\", \\\"receiver\\\": \\\"di_agent\\\", \\\"model\\\": null, \\\"markdown\\\": \\\"Read the result data of the dependent steps in the above historical message to complete the current goal:根据获取的数据源信息和字段信息，生成集成任务配置内容，包括数据源ID、目标ID、表名和字段映射。\\\", \\\"resource\\\": null}, {\\\"sender\\\": \\\"di_agent\\\", \\\"receiver\\\": \\\"PlanManager\\\", \\\"model\\\": \\\"deepseek-r1-distill-qwen\\\", \\\"markdown\\\": \\\"{'processName': 'ods_ceshi620_to_dwd_ceshi620', 'name': 'ods_ceshi620_to_dwd_ceshi620', 'columnMapType': '0', 'writeMode': '0', 'sourceId': '1', 'sinkId': '2', 'sourceDatabase': 'noah', 'sinkDatabase': 'noah', 'sourceType': '5', 'sinkType': '5', 'sourceTable': 'ods_ceshi620', 'sinkTable': 'dwd_ceshi620'}\\\", \\\"resource\\\": null}]\\n```\"}, {\"name\": \"调用doCreateDiProcess API创建集成任务并运行工作流。\", \"num\": 10, \"status\": \"complete\", \"agent\": \"ToolExpert\", \"markdown\": \"```agent-messages\\n[{\\\"sender\\\": \\\"PlanManager\\\", \\\"receiver\\\": \\\"ToolExpert\\\", \\\"model\\\": null, \\\"markdown\\\": \\\"Read the result data of the dependent steps in the above historical message to complete the current goal:调用doCreateDiProcess API创建集成任务并运行工作流。\\\", \\\"resource\\\": null}, {\\\"sender\\\": \\\"ToolExpert\\\", \\\"receiver\\\": \\\"PlanManager\\\", \\\"model\\\": \\\"deepseek-r1-distill-qwen\\\", \\\"markdown\\\": \\\"```vis-plugin\\\\n{\\\\\\\"name\\\\\\\": \\\\\\\"doCreateDiProcess\\\\\\\", \\\\\\\"args\\\\\\\": {\\\\\\\"diTaskParams\\\\\\\": {\\\\\\\"processName\\\\\\\": \\\\\\\"ods_ceshi620_to_dwd_ceshi620\\\\\\\", \\\\\\\"name\\\\\\\": \\\\\\\"ods_ceshi620_to_dwd_ceshi620\\\\\\\", \\\\\\\"columnMapType\\\\\\\": \\\\\\\"0\\\\\\\", \\\\\\\"writeMode\\\\\\\": \\\\\\\"0\\\\\\\", \\\\\\\"sourceId\\\\\\\": \\\\\\\"1\\\\\\\", \\\\\\\"sinkId\\\\\\\": \\\\\\\"2\\\\\\\", \\\\\\\"sourceDatabase\\\\\\\": \\\\\\\"noah\\\\\\\", \\\\\\\"sinkDatabase\\\\\\\": \\\\\\\"noah\\\\\\\", \\\\\\\"sourceType\\\\\\\": \\\\\\\"5\\\\\\\", \\\\\\\"sinkType\\\\\\\": \\\\\\\"5\\\\\\\", \\\\\\\"sourceTable\\\\\\\": \\\\\\\"ods_ceshi620\\\\\\\", \\\\\\\"sinkTable\\\\\\\": \\\\\\\"dwd_ceshi620\\\\\\\"}}, \\\\\\\"status\\\\\\\": \\\\\\\"complete\\\\\\\", \\\\\\\"logo\\\\\\\": null, \\\\\\\"result\\\\\\\": \\\\\\\"meta=None content=[TextContent(type='text', text='Cannot invoke \\\\\\\\\\\\\\\"com.alibaba.fastjson2.JSONObject.getJSONArray(String)\\\\\\\\\\\\\\\" because the return value of \\\\\\\\\\\\\\\"com.alibaba.fastjson2.JSONObject.getJSONObject(String)\\\\\\\\\\\\\\\" is null', annotations=None)] isError=True\\\\\\\", \\\\\\\"err_msg\\\\\\\": null}\\\\n```\\\", \\\"resource\\\": null}]\\n```\"}]\n```\n```agent-messages\n[{\"sender\": \"ToolExpert\", \"receiver\": \"PlanManager\", \"model\": \"deepseek-r1-distill-qwen\", \"markdown\": \"```vis-plugin\\n{\\\"name\\\": \\\"doCreateDiProcess\\\", \\\"args\\\": {\\\"diTaskParams\\\": {\\\"processName\\\": \\\"ods_ceshi620_to_dwd_ceshi620\\\", \\\"name\\\": \\\"ods_ceshi620_to_dwd_ceshi620\\\", \\\"columnMapType\\\": \\\"0\\\", \\\"writeMode\\\": \\\"0\\\", \\\"sourceId\\\": \\\"1\\\", \\\"sinkId\\\": \\\"2\\\", \\\"sourceDatabase\\\": \\\"noah\\\", \\\"sinkDatabase\\\": \\\"noah\\\", \\\"sourceType\\\": \\\"5\\\", \\\"sinkType\\\": \\\"5\\\", \\\"sourceTable\\\": \\\"ods_ceshi620\\\", \\\"sinkTable\\\": \\\"dwd_ceshi620\\\"}}, \\\"status\\\": \\\"complete\\\", \\\"logo\\\": null, \\\"result\\\": \\\"meta=None content=[TextContent(type='text', text='Cannot invoke \\\\\\\"com.alibaba.fastjson2.JSONObject.getJSONArray(String)\\\\\\\" because the return value of \\\\\\\"com.alibaba.fastjson2.JSONObject.getJSONObject(String)\\\\\\\" is null', annotations=None)] isError=True\\\", \\\"err_msg\\\": null}\\n```\", \"resource\": null}]\n```",
            "order": 1,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "human",
            "context": "创建离线同步任务，数据来源是SC，目标源是SC，数据来源表是ods_ceshi620，数据目标表是dwd_ceshi620，数据全量同步，创建工作流并上线",
            "order": 2,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "view",
            "context": "```agent-plans\n[{\"name\": \"创建离线同步任务，数据来源是SC，目标源是SC，数据来源表是ods_ceshi620，数据目标表是dwd_ceshi620，数据全量同步，创建工作流并上线\", \"num\": 2, \"status\": \"complete\", \"agent\": \"PlanManager\", \"markdown\": \"```agent-messages\\n[{\\\"sender\\\": \\\"Human\\\", \\\"receiver\\\": \\\"PlanManager\\\", \\\"model\\\": null, \\\"markdown\\\": \\\"创建离线同步任务，数据来源是SC，目标源是SC，数据来源表是ods_ceshi620，数据目标表是dwd_ceshi620，数据全量同步，创建工作流并上线\\\", \\\"resource\\\": null}, {\\\"sender\\\": \\\"PlanManager\\\", \\\"receiver\\\": \\\"Human\\\", \\\"model\\\": null, \\\"markdown\\\": \\\"```vis-plugin\\\\n{\\\\\\\"name\\\\\\\": \\\\\\\"doCreateDiProcess\\\\\\\", \\\\\\\"args\\\\\\\": {\\\\\\\"diTaskParams\\\\\\\": {\\\\\\\"processName\\\\\\\": \\\\\\\"DefaultProcessName\\\\\\\", \\\\\\\"name\\\\\\\": \\\\\\\"DefaultTaskName\\\\\\\", \\\\\\\"columnMapType\\\\\\\": \\\\\\\"0\\\\\\\", \\\\\\\"writeMode\\\\\\\": \\\\\\\"0\\\\\\\", \\\\\\\"sourceId\\\\\\\": \\\\\\\"8\\\\\\\", \\\\\\\"sinkId\\\\\\\": \\\\\\\"8\\\\\\\", \\\\\\\"sourceDatabase\\\\\\\": \\\\\\\"noah_dq\\\\\\\", \\\\\\\"sinkDatabase\\\\\\\": \\\\\\\"noah_dq\\\\\\\", \\\\\\\"sourceType\\\\\\\": \\\\\\\"6\\\\\\\", \\\\\\\"sinkType\\\\\\\": \\\\\\\"6\\\\\\\", \\\\\\\"sourceTable\\\\\\\": \\\\\\\"ods_ceshi620\\\\\\\", \\\\\\\"sinkTable\\\\\\\": \\\\\\\"dwd_ceshi620\\\\\\\"}}, \\\\\\\"status\\\\\\\": \\\\\\\"complete\\\\\\\", \\\\\\\"logo\\\\\\\": null, \\\\\\\"result\\\\\\\": \\\\\\\"meta=None content=[TextContent(type='text', text='\\\\\\\\\\\\\\\"{\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"code\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":200,\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"data\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":18123070742848,\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"msg\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\",\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"message\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"}\\\\\\\\\\\\\\\"', annotations=None)] isError=False\\\\\\\", \\\\\\\"err_msg\\\\\\\": null}\\\\n```\\\", \\\"resource\\\": null}]\\n```\"}]\n```\n```agent-messages\n[{\"sender\": \"Planner\", \"receiver\": \"PlanManager\", \"model\": \"deepseek-r1-distill-qwen\", \"markdown\": \"- 1.调用getDatasourceInfo API查询数据源SC的信息，获取数据源id、ip、账号、密码、数据库等连接信息。[ToolExpert]\\n- 2.根据用户提供的信息生成集成任务配置内容，包括数据来源表ods_ceshi620和数据目标表dwd_ceshi620的全量同步配置。[di_agent]\\n- 3.调用doCreateDiProcess API创建集成任务并运行工作流，参数为步骤2生成的diTaskParams。[ToolExpert]\", \"resource\": null}]\n```\n```agent-plans\n[{\"name\": \"调用getDatasourceInfo API查询数据源SC的信息，获取数据源id、ip、账号、密码、数据库等连接信息。\", \"num\": 2, \"status\": \"complete\", \"agent\": \"ToolExpert\", \"markdown\": \"```agent-messages\\n[{\\\"sender\\\": \\\"PlanManager\\\", \\\"receiver\\\": \\\"ToolExpert\\\", \\\"model\\\": null, \\\"markdown\\\": \\\"调用getDatasourceInfo API查询数据源SC的信息，获取数据源id、ip、账号、密码、数据库等连接信息。\\\", \\\"resource\\\": null}, {\\\"sender\\\": \\\"ToolExpert\\\", \\\"receiver\\\": \\\"PlanManager\\\", \\\"model\\\": \\\"deepseek-r1-distill-qwen\\\", \\\"markdown\\\": \\\"```vis-plugin\\\\n{\\\\\\\"name\\\\\\\": \\\\\\\"getDatasourceInfo\\\\\\\", \\\\\\\"args\\\\\\\": {\\\\\\\"name\\\\\\\": \\\\\\\"SC\\\\\\\"}, \\\\\\\"status\\\\\\\": \\\\\\\"complete\\\\\\\", \\\\\\\"logo\\\\\\\": null, \\\\\\\"result\\\\\\\": \\\\\\\"meta=None content=[TextContent(type='text', text='\\\\\\\\\\\\\\\"[{\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"id\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":8,\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"userId\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":0,\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"name\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"SC\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\",\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"type\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":6,\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"externalDatasourceConfig\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"{\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"database\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"noah_dq\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\",\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"ipAddress\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"192.168.6.201\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\",\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"port\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"8123\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\",\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"user\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"admin\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\",\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"password\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"admin\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\",\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"other\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":{},\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"customerPro\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":{}}\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"}]\\\\\\\\\\\\\\\"', 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'sourceTable': 'ods_ceshi620', 'sinkTable': 'dwd_ceshi620'}\\\", \\\"resource\\\": null}]\\n```\"}, {\"name\": \"调用doCreateDiProcess API创建集成任务并运行工作流，参数为步骤2生成的diTaskParams。\", \"num\": 6, \"status\": \"complete\", \"agent\": \"ToolExpert\", \"markdown\": \"```agent-messages\\n[{\\\"sender\\\": \\\"PlanManager\\\", \\\"receiver\\\": \\\"ToolExpert\\\", \\\"model\\\": null, \\\"markdown\\\": \\\"Read the result data of the dependent steps in the above historical message to complete the current goal:调用doCreateDiProcess API创建集成任务并运行工作流，参数为步骤2生成的diTaskParams。\\\", \\\"resource\\\": null}, {\\\"sender\\\": \\\"ToolExpert\\\", \\\"receiver\\\": \\\"PlanManager\\\", \\\"model\\\": \\\"deepseek-r1-distill-qwen\\\", \\\"markdown\\\": \\\"```vis-plugin\\\\n{\\\\\\\"name\\\\\\\": \\\\\\\"doCreateDiProcess\\\\\\\", \\\\\\\"args\\\\\\\": {\\\\\\\"diTaskParams\\\\\\\": {\\\\\\\"processName\\\\\\\": \\\\\\\"DefaultProcessName\\\\\\\", \\\\\\\"name\\\\\\\": \\\\\\\"DefaultTaskName\\\\\\\", \\\\\\\"columnMapType\\\\\\\": \\\\\\\"0\\\\\\\", \\\\\\\"writeMode\\\\\\\": \\\\\\\"0\\\\\\\", \\\\\\\"sourceId\\\\\\\": \\\\\\\"8\\\\\\\", \\\\\\\"sinkId\\\\\\\": \\\\\\\"8\\\\\\\", \\\\\\\"sourceDatabase\\\\\\\": \\\\\\\"noah_dq\\\\\\\", \\\\\\\"sinkDatabase\\\\\\\": \\\\\\\"noah_dq\\\\\\\", \\\\\\\"sourceType\\\\\\\": \\\\\\\"6\\\\\\\", \\\\\\\"sinkType\\\\\\\": \\\\\\\"6\\\\\\\", \\\\\\\"sourceTable\\\\\\\": \\\\\\\"ods_ceshi620\\\\\\\", \\\\\\\"sinkTable\\\\\\\": \\\\\\\"dwd_ceshi620\\\\\\\"}}, \\\\\\\"status\\\\\\\": \\\\\\\"complete\\\\\\\", \\\\\\\"logo\\\\\\\": null, \\\\\\\"result\\\\\\\": \\\\\\\"meta=None content=[TextContent(type='text', text='\\\\\\\\\\\\\\\"{\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"code\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":200,\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"data\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":18123070742848,\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"msg\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\",\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"message\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\":\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\"}\\\\\\\\\\\\\\\"', annotations=None)] isError=False\\\\\\\", \\\\\\\"err_msg\\\\\\\": null}\\\\n```\\\", \\\"resource\\\": null}]\\n```\"}]\n```\n```agent-messages\n[{\"sender\": \"ToolExpert\", \"receiver\": \"PlanManager\", \"model\": \"deepseek-r1-distill-qwen\", \"markdown\": \"```vis-plugin\\n{\\\"name\\\": \\\"doCreateDiProcess\\\", \\\"args\\\": {\\\"diTaskParams\\\": {\\\"processName\\\": \\\"DefaultProcessName\\\", \\\"name\\\": \\\"DefaultTaskName\\\", \\\"columnMapType\\\": \\\"0\\\", \\\"writeMode\\\": \\\"0\\\", \\\"sourceId\\\": \\\"8\\\", \\\"sinkId\\\": \\\"8\\\", \\\"sourceDatabase\\\": \\\"noah_dq\\\", \\\"sinkDatabase\\\": \\\"noah_dq\\\", \\\"sourceType\\\": \\\"6\\\", \\\"sinkType\\\": \\\"6\\\", \\\"sourceTable\\\": \\\"ods_ceshi620\\\", \\\"sinkTable\\\": \\\"dwd_ceshi620\\\"}}, \\\"status\\\": \\\"complete\\\", \\\"logo\\\": null, \\\"result\\\": \\\"meta=None content=[TextContent(type='text', text='\\\\\\\"{\\\\\\\\\\\\\\\\\\\\\\\"code\\\\\\\\\\\\\\\\\\\\\\\":200,\\\\\\\\\\\\\\\\\\\\\\\"data\\\\\\\\\\\\\\\\\\\\\\\":18123070742848,\\\\\\\\\\\\\\\\\\\\\\\"msg\\\\\\\\\\\\\\\\\\\\\\\":\\\\\\\\\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\\\\\\\\\",\\\\\\\\\\\\\\\\\\\\\\\"message\\\\\\\\\\\\\\\\\\\\\\\":\\\\\\\\\\\\\\\\\\\\\\\"\\\\\\\\\\\\\\\\\\\\\\\"}\\\\\\\"', annotations=None)] isError=False\\\", \\\"err_msg\\\": null}\\n```\", \"resource\": null}]\n```",
            "order": 2,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        }
    ]
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// 指标 历史对话： 定制开发的 ：同环比，排名分析图形
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            "order": 1,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "view",
            "context": "\n```agent-messages\n[{\"sender\": \"IdxScientist\", \"receiver\": \"Human\", \"model\": \"deepseek-r1-distill-qwen\", \"markdown\": \"```vis-basic-card\\n{\\\"type\\\": \\\"VisBasicCard\\\", \\\"metricModelId\\\": 46, \\\"metricColumnName\\\": \\\"289:hour\\\", \\\"question\\\": \\\"研发项目工时的同比增长率是多少,研发项目的工时分布如何,研发项目的工时趋势预测\\\", \\\"data\\\": \\\"[{\\\\\\\"title\\\\\\\": \\\\\\\"\\\\\\\\u7814\\\\\\\\u53d1\\\\\\\\u9879\\\\\\\\u76ee\\\\\\\\u5de5\\\\\\\\u65f6\\\\\\\", \\\\\\\"value\\\\\\\": \\\\\\\"1390.00\\\\\\\", \\\\\\\"date\\\\\\\": \\\\\\\"2025-04\\\\\\\", \\\\\\\"periodPercent\\\\\\\": \\\\\\\"138,900%\\\\\\\", \\\\\\\"roundPercent\\\\\\\": \\\\\\\"138,900%\\\\\\\", \\\\\\\"periodValue\\\\\\\": \\\\\\\"1389.00\\\\\\\", \\\\\\\"roundValue\\\\\\\": \\\\\\\"1389.00\\\\\\\"}]\\\"}\\n```\", \"resource\": null}]\n```",
            "order": 1,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "human",
            "context": "25年4月在研发的项目上的工时花费同环比分析",
            "order": 2,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "view",
            "context": "\n```agent-messages\n[{\"sender\": \"IdxScientist\", \"receiver\": \"Human\", \"model\": \"deepseek-r1-distill-qwen\", \"markdown\": \"```vis-basic-card\\n{\\\"type\\\": \\\"VisBasicCard\\\", \\\"metricModelId\\\": 46, \\\"metricColumnName\\\": \\\"289:hour\\\", \\\"question\\\": \\\"研发项目工时的同比增长率是多少,研发项目的工时分布如何,研发项目的工时趋势预测\\\", \\\"data\\\": \\\"[{\\\\\\\"title\\\\\\\": \\\\\\\"\\\\\\\\u7814\\\\\\\\u53d1\\\\\\\\u9879\\\\\\\\u76ee\\\\\\\\u5de5\\\\\\\\u65f6\\\\\\\", \\\\\\\"value\\\\\\\": \\\\\\\"1390.00\\\\\\\", \\\\\\\"date\\\\\\\": \\\\\\\"2025-04\\\\\\\", \\\\\\\"periodPercent\\\\\\\": \\\\\\\"138,900%\\\\\\\", \\\\\\\"roundPercent\\\\\\\": \\\\\\\"138,900%\\\\\\\", \\\\\\\"periodValue\\\\\\\": \\\\\\\"1389.00\\\\\\\", \\\\\\\"roundValue\\\\\\\": \\\\\\\"1389.00\\\\\\\"}]\\\"}\\n```\", \"resource\": null}]\n```",
            "order": 2,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "human",
            "context": "25年4月在研发的项目上工时花费",
            "order": 3,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "view",
            "context": "\n```agent-messages\n[{\"sender\": \"IdxScientist\", \"receiver\": \"Human\", \"model\": \"deepseek-r1-distill-qwen\", \"markdown\": \"Error:Check your answers, the sql run failed!Reason:(pymysql.err.OperationalError) (5502, \\\"Getting analyzing error. Detail message: Unknown table 'time.project_dimension_mv'.\\\")\\n[SQL: SELECT w.t_289_month AS 月份, SUM(w.sum_t_289_hour) AS 总工时 FROM time.working_hours_efficiency_mv w JOIN time.project_dimension_mv p ON w.t_289_project_id = p.t_293_id WHERE w.t_289_year = 2025 AND w.t_289_month = 4 AND p.t_293_name = '研发项目' GROUP BY w.t_289_month;]\\n(Background on this error at: https://sqlalche.me/e/20/e3q8)\", \"resource\": null}]\n```",
            "order": 3,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "human",
            "context": "25年四月在研发项目上的工时花费同环比分析",
            "order": 4,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "view",
            "context": "\n```agent-messages\n[{\"sender\": \"IdxScientist\", \"receiver\": \"Human\", \"model\": \"deepseek-r1-distill-qwen\", \"markdown\": \"```vis-basic-card\\n{\\\"type\\\": \\\"VisBasicCard\\\", \\\"metricModelId\\\": 46, \\\"metricColumnName\\\": \\\"289:hour\\\", \\\"question\\\": \\\"研发项目工时的同比增长率是多少,研发项目的工时分布如何,研发项目的工时趋势预测\\\", \\\"data\\\": \\\"[{\\\\\\\"title\\\\\\\": \\\\\\\"\\\\\\\\u7814\\\\\\\\u53d1\\\\\\\\u9879\\\\\\\\u76ee\\\\\\\\u5de5\\\\\\\\u65f6\\\\\\\", \\\\\\\"value\\\\\\\": \\\\\\\"1390.00\\\\\\\", \\\\\\\"date\\\\\\\": \\\\\\\"2025-04\\\\\\\", \\\\\\\"periodPercent\\\\\\\": \\\\\\\"138,900%\\\\\\\", \\\\\\\"roundPercent\\\\\\\": \\\\\\\"138,900%\\\\\\\", \\\\\\\"periodValue\\\\\\\": \\\\\\\"1389.00\\\\\\\", \\\\\\\"roundValue\\\\\\\": \\\\\\\"1389.00\\\\\\\"}]\\\"}\\n```\", \"resource\": null}]\n```",
            "order": 4,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "human",
            "context": "造成影响的原因是什么？",
            "order": 5,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "view",
            "context": "\n```agent-messages\n[{\"sender\": \"IdxScientist\", \"receiver\": \"Human\", \"model\": \"deepseek-r1-distill-qwen\", \"markdown\": \"```vis-attribution-report\\n{\\\"metricModelId\\\": 46, \\\"metricColumnName\\\": \\\"289:hour\\\", \\\"question\\\": \\\"哪些项目对工时有显著影响,工时变化的主要驱动因素是什么,工时与工资的相关性如何\\\", \\\"data\\\": \\\"{\\\\\\\"analysisData\\\\\\\": {\\\\\\\"2025\\\\\\\": \\\\\\\"1391.00\\\\\\\", \\\\\\\"2024\\\\\\\": \\\\\\\"1.00\\\\\\\"}, \\\\\\\"analysisTotal\\\\\\\": \\\\\\\"1390.00 (139,000%)\\\\\\\", \\\\\\\"dimensionColumnsList\\\\\\\": {\\\\\\\"\\\\\\\\u7528\\\\\\\\u6237\\\\\\\\u540d\\\\\\\": [{\\\\\\\"contributionPercent\\\\\\\": \\\\\\\"100%\\\\\\\", \\\\\\\"dimensionColumnName\\\\\\\": \\\\\\\"\\\\\\\\u718a\\\\\\\\u5fd7\\\\\\\\u6770\\\\\\\", \\\\\\\"dimensionData\\\\\\\": {\\\\\\\"2025\\\\\\\": \\\\\\\"4.00\\\\\\\", \\\\\\\"2024\\\\\\\": \\\\\\\"1.00\\\\\\\"}}], \\\\\\\"\\\\\\\\u9879\\\\\\\\u76eeID\\\\\\\": [{\\\\\\\"contributionPercent\\\\\\\": \\\\\\\"100%\\\\\\\", \\\\\\\"dimensionColumnName\\\\\\\": \\\\\\\"2101\\\\\\\", \\\\\\\"dimensionData\\\\\\\": {\\\\\\\"2025\\\\\\\": \\\\\\\"41.50\\\\\\\", \\\\\\\"2024\\\\\\\": \\\\\\\"1.00\\\\\\\"}}]}}\\\"}\\n```\", \"resource\": null}]\n```",
            "order": 5,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "human",
            "context": "造成影响的原因是什么？",
            "order": 6,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "view",
            "context": "\n```agent-messages\n[{\"sender\": \"IdxScientist\", \"receiver\": \"Human\", \"model\": \"deepseek-r1-distill-qwen\", \"markdown\": \"```vis-attribution-report\\n{\\\"metricModelId\\\": 46, \\\"metricColumnName\\\": \\\"289:hour\\\", \\\"question\\\": \\\"哪些项目对工时有显著影响,工时变化的主要驱动因素是什么,工时与工资的相关性如何\\\", \\\"data\\\": \\\"{\\\\\\\"analysisData\\\\\\\": {\\\\\\\"2025\\\\\\\": \\\\\\\"1391.00\\\\\\\", \\\\\\\"2024\\\\\\\": \\\\\\\"1.00\\\\\\\"}, \\\\\\\"analysisTotal\\\\\\\": \\\\\\\"1390.00 (139,000%)\\\\\\\", \\\\\\\"dimensionColumnsList\\\\\\\": {\\\\\\\"\\\\\\\\u7528\\\\\\\\u6237\\\\\\\\u540d\\\\\\\": [{\\\\\\\"contributionPercent\\\\\\\": \\\\\\\"100%\\\\\\\", \\\\\\\"dimensionColumnName\\\\\\\": \\\\\\\"\\\\\\\\u718a\\\\\\\\u5fd7\\\\\\\\u6770\\\\\\\", \\\\\\\"dimensionData\\\\\\\": {\\\\\\\"2025\\\\\\\": \\\\\\\"4.00\\\\\\\", \\\\\\\"2024\\\\\\\": \\\\\\\"1.00\\\\\\\"}}], \\\\\\\"\\\\\\\\u9879\\\\\\\\u76eeID\\\\\\\": [{\\\\\\\"contributionPercent\\\\\\\": \\\\\\\"100%\\\\\\\", \\\\\\\"dimensionColumnName\\\\\\\": \\\\\\\"2101\\\\\\\", \\\\\\\"dimensionData\\\\\\\": {\\\\\\\"2025\\\\\\\": \\\\\\\"41.50\\\\\\\", \\\\\\\"2024\\\\\\\": \\\\\\\"1.00\\\\\\\"}}]}}\\\"}\\n```\", \"resource\": null}]\n```",
            "order": 6,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "human",
            "context": "25年四月在研发的任务上工时花费同环比分析",
            "order": 7,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "view",
            "context": "\n```agent-messages\n[{\"sender\": \"IdxScientist\", \"receiver\": \"Human\", \"model\": \"deepseek-r1-distill-qwen\", \"markdown\": \"```vis-basic-card\\n{\\\"type\\\": \\\"VisBasicCard\\\", \\\"metricModelId\\\": 46, \\\"metricColumnName\\\": \\\"289:hour\\\", \\\"question\\\": \\\"研发任务的工时趋势如何,哪些项目对研发任务的工时有显著影响,研发任务的工时与工资的相关性如何\\\", \\\"data\\\": \\\"[{\\\\\\\"title\\\\\\\": \\\\\\\"\\\\\\\\u5de5\\\\\\\\u65f6\\\\\\\", \\\\\\\"value\\\\\\\": \\\\\\\"1390.00\\\\\\\", \\\\\\\"date\\\\\\\": \\\\\\\"2025-04\\\\\\\", \\\\\\\"periodPercent\\\\\\\": \\\\\\\"138,900%\\\\\\\", \\\\\\\"roundPercent\\\\\\\": \\\\\\\"138,900%\\\\\\\", \\\\\\\"periodValue\\\\\\\": \\\\\\\"1389.00\\\\\\\", \\\\\\\"roundValue\\\\\\\": \\\\\\\"1389.00\\\\\\\"}]\\\"}\\n```\", \"resource\": null}]\n```",
            "order": 7,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "human",
            "context": "25年4月在研发的任务上工时花费同环比分析",
            "order": 8,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "view",
            "context": "\n```agent-messages\n[{\"sender\": \"IdxScientist\", \"receiver\": \"Human\", \"model\": \"deepseek-r1-distill-qwen\", \"markdown\": \"```vis-basic-card\\n{\\\"type\\\": \\\"VisBasicCard\\\", \\\"metricModelId\\\": 46, \\\"metricColumnName\\\": \\\"289:hour\\\", \\\"question\\\": \\\"研发任务的工时趋势如何,哪些项目对研发任务的工时有显著影响,研发任务的工时与工资的相关性如何\\\", \\\"data\\\": \\\"[{\\\\\\\"title\\\\\\\": \\\\\\\"\\\\\\\\u5de5\\\\\\\\u65f6\\\\\\\", \\\\\\\"value\\\\\\\": \\\\\\\"1390.00\\\\\\\", \\\\\\\"date\\\\\\\": \\\\\\\"2025-04\\\\\\\", \\\\\\\"periodPercent\\\\\\\": \\\\\\\"138,900%\\\\\\\", \\\\\\\"roundPercent\\\\\\\": \\\\\\\"138,900%\\\\\\\", \\\\\\\"periodValue\\\\\\\": \\\\\\\"1389.00\\\\\\\", \\\\\\\"roundValue\\\\\\\": \\\\\\\"1389.00\\\\\\\"}]\\\"}\\n```\", \"resource\": null}]\n```",
            "order": 8,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "human",
            "context": "25年四月员工请假调休次数和正常时间的对比",
            "order": 9,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "view",
            "context": "\n```agent-messages\n[{\"sender\": \"IdxScientist\", \"receiver\": \"Human\", \"model\": \"deepseek-r1-distill-qwen\", \"markdown\": \"出问题了，请刷新后查询！\", \"resource\": null}]\n```",
            "order": 9,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        }
    ]
}
// 模型问答： 历史对话
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    "err_code": null,
    "err_msg": null,
    "err_msg_zh": null,
    "data": [
        {
            "role": "human",
            "context": "使用airport_operations和flight_operation两张表创建一个航班延误分析模型。\n",
            "order": 1,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "ai",
            "context": "\n\n```json\n{\n    \"thoughts\": \"根据用户的问题，我需要创建一个航班延误分析模型，使用airport_operations和flight_operation两张表。首先，我查看了这两张表的结构和字段定义，发现它们都包含航班号字段，可以用来关联。然后，我确定了需要使用的字段，包括计划起飞时间、实际起飞时间、航班号、起飞延误时间和航班状态。接着，我考虑了如何计算延误率和平均延误时间，延误率可以通过计算延误的航班数除以总航班数得到，平均延误时间可以通过计算所有延误航班的起飞延误时间的平均值得到。最后，我生成了一个包含这两个指标的SQL查询，并确保了时间字段的正确性和查询性能的优化。\",\n    \"metric_info\": {\n        \"name\": \"航班延误分析模型\",\n        \"nameEng\": \"flight_delay_analysis\",\n        \"comment\": \"分析航班延误情况，包括延误率和平均延误时间\",\n        \"statisticalGranularity\": \"D\",\n        \"polymerizationMode\": \"AVG,COUNT\",\n        \"metricModelTables\": [\n            {\n                \"name\": \"airport_operations\",\n                \"comment\": \"航班操作表\",\n                \"querySqlId\": \"\",\n                \"querySql\": \"\",\n                \"tableType\": 0,\n                \"metricModelTableColumns\": [\n                    {\n                        \"name\": \"date\",\n                        \"comment\": \"航班日期\",\n                        \"metricType\": \"Dimension\",\n                        \"isTimeDimension\": true,\n                        \"precision\": 0\n                    },\n                    {\n                        \"name\": \"flight_number\",\n                        \"comment\": \"航班号\",\n                        \"metricType\": \"Dimension\",\n                        \"isTimeDimension\": false,\n                        \"precision\": 0\n                    }\n                ]\n            },\n            {\n                \"name\": \"flight_operation\",\n                \"comment\": \"航班操作表\",\n                \"querySqlId\": \"\",\n                \"querySql\": \"\",\n                \"tableType\": 0,\n                \"metricModelTableColumns\": [\n                    {\n                        \"name\": \"TakeOffDelayTime_R\",\n                        \"comment\": \"起飞延误时间（分钟）\",\n                        \"metricType\": \"Metrics\",\n                        \"isTimeDimension\": false,\n                        \"precision\": 0\n                    },\n                    {\n                        \"name\": \"name\",\n                        \"comment\": \"航班号\",\n                        \"metricType\": \"Dimension\",\n                        \"isTimeDimension\": false,\n                        \"precision\": 0\n                    }\n                ]\n            }\n        ],\n        \"metricModelTableRelationships\": [\n            {\n                \"sourceTableName\": \"airport_operations\",\n                \"sourceColumn\": \"flight_number\",\n                \"joinType\": \"INNER_JOIN\",\n                \"targetTableName\": \"flight_operation\",\n                \"targetColumn\": \"name\"\n            }\n        ]\n    },\n    \"sql\": \"SELECT a.date, COUNT(DISTINCT a.flight_number) AS total_flights, COUNT(DISTINCT CASE WHEN f.TakeOffDelayTime_R > 0 THEN a.flight_number END) AS delayed_flights, (COUNT(DISTINCT CASE WHEN f.TakeOffDelayTime_R > 0 THEN a.flight_number END) / COUNT(DISTINCT a.flight_number)) * 100 AS delay_rate, AVG(CAST(f.TakeOffDelayTime_R AS DECIMAL)) AS average_delay_time FROM airport_operations a INNER JOIN flight_operation f ON a.flight_number = f.name GROUP BY a.date;\"\n}\n```",
            "order": 1,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "view",
            "context": "```vis-thinking\\n\\n好的，我现在需要帮助用户创建一个航班延误分析模型，使用的是airport_operations和flight_operation两张表。首先，我得仔细分析用户的问题，理解他们的需求和可能遇到的挑战。\\n\\n首先，用户明确提到要使用这两张表，所以我需要查看这两张表的结构和字段定义，确保它们能够提供所需的数据。根据提供的表结构，airport_operations包含航班的基本信息，如日期、计划起飞时间、实际起飞时间、航班号、起飞和降落机场以及乘客人数等。flight_operation则包含延误时间相关的字段，如起飞延误时间、航班总延误时间等，以及航班状态和类型。\\n\\n接下来，我需要明确航班延误分析模型的目标。根据用户的问题，这个模型应该分析航班延误情况，包括延误率和平均延误时间。因此，我需要计算这两个指标，并确保数据来源正确。\\n\\n计算延误率需要知道延误的航班数量和总航班数量。延误的航班可以通过flight_operation表中的延误时间字段来判断，例如起飞延误时间是否大于0。总航班数量可以通过airport_operations表中的航班号统计得出。\\n\\n平均延误时间则需要计算所有延误航班的延误时间总和，然后除以延误航班的数量。这里需要注意的是，延误时间可能在多个字段中记录，如TakeOffDelayTime_R和FlightDelayTime_R，需要明确使用哪个字段作为平均延误时间的依据。\\n\\n接下来，我需要考虑如何关联这两张表。根据指标模型定义，两张表通过航班号进行关联，airport_operations中的flight_number与flight_operation中的name字段对应。因此，在SQL查询中，我需要使用INNER JOIN来连接这两张表，确保数据准确。\\n\\n在编写SQL时，我需要确保时间字段正确。根据约束，SQL必须包含一个可解释业务周期的时间字段，这里选择airport_operations中的date字段，类型为DATE，符合要求。\\n\\n然后，我需要计算延误率和平均延误时间。延误率可以通过计算延误航班数除以总航班数得到，使用COUNT和SUM函数。平均延误时间则使用AVG函数计算TakeOffDelayTime_R字段的平均值。\\n\\n在生成指标模型时，需要确保结构正确。metricModelTables部分包括两张表的信息，各自的字段定义和度量类型。metricModelTableRelationships部分需要明确关联方式，这里是INNER JOIN。\\n\\n最后，我需要验证生成的SQL是否正确，确保所有字段都正确引用，没有语法错误。同时，检查指标模型是否符合要求，特别是时间维度和聚合方式是否正确。\\n\\n整个过程中，我需要确保遵循注释优先原则，正确解析字段含义，避免混淆字段名称和注释内容。同时，严格按照约束生成SQL和指标模型，确保数据准确性和查询性能优化。\\n\\n```\\n根据用户的问题，我需要创建一个航班延误分析模型，使用airport_operations和flight_operation两张表。首先，我查看了这两张表的结构和字段定义，发现它们都包含航班号字段，可以用来关联。然后，我确定了需要使用的字段，包括计划起飞时间、实际起飞时间、航班号、起飞延误时间和航班状态。接着，我考虑了如何计算延误率和平均延误时间，延误率可以通过计算延误的航班数除以总航班数得到，平均延误时间可以通过计算所有延误航班的起飞延误时间的平均值得到。最后，我生成了一个包含这两个指标的SQL查询，并确保了时间字段的正确性和查询性能的优化。\\n```json\\n{\\n  \"name\": \"航班延误分析模型\",\\n  \"nameEng\": \"flight_delay_analysis\",\\n  \"comment\": \"分析航班延误情况，包括延误率和平均延误时间\",\\n  \"statisticalGranularity\": \"D\",\\n  \"polymerizationMode\": \"AVG,COUNT\",\\n  \"metricModelTables\": [\\n    {\\n      \"name\": \"airport_operations\",\\n      \"comment\": \"航班操作表\",\\n      \"querySqlId\": \"\",\\n      \"querySql\": \"\",\\n      \"tableType\": 0,\\n      \"metricModelTableColumns\": [\\n        {\\n          \"name\": \"date\",\\n          \"comment\": \"航班日期\",\\n          \"metricType\": \"Dimension\",\\n          \"isTimeDimension\": true,\\n          \"precision\": 0\\n        },\\n        {\\n          \"name\": \"flight_number\",\\n          \"comment\": \"航班号\",\\n          \"metricType\": \"Dimension\",\\n          \"isTimeDimension\": false,\\n          \"precision\": 0\\n        }\\n      ]\\n    },\\n    {\\n      \"name\": \"flight_operation\",\\n      \"comment\": \"航班操作表\",\\n      \"querySqlId\": \"\",\\n      \"querySql\": \"\",\\n      \"tableType\": 0,\\n      \"metricModelTableColumns\": [\\n        {\\n          \"name\": \"TakeOffDelayTime_R\",\\n          \"comment\": \"起飞延误时间（分钟）\",\\n          \"metricType\": \"Metrics\",\\n          \"isTimeDimension\": false,\\n          \"precision\": 0\\n        },\\n        {\\n          \"name\": \"name\",\\n          \"comment\": \"航班号\",\\n          \"metricType\": \"Dimension\",\\n          \"isTimeDimension\": false,\\n          \"precision\": 0\\n        }\\n      ]\\n    }\\n  ],\\n  \"metricModelTableRelationships\": [\\n    {\\n      \"sourceTableName\": \"airport_operations\",\\n      \"sourceColumn\": \"flight_number\",\\n      \"joinType\": \"INNER_JOIN\",\\n      \"targetTableName\": \"flight_operation\",\\n      \"targetColumn\": \"name\"\\n    }\\n  ]\\n}\\n```\\n```sql\\nSELECT a.date,\\n       COUNT(DISTINCT a.flight_number) AS total_flights,\\n       COUNT(DISTINCT CASE\\n                          WHEN f.TakeOffDelayTime_R > 0 THEN a.flight_number\\n                      END) AS delayed_flights,\\n       (COUNT(DISTINCT CASE\\n                           WHEN f.TakeOffDelayTime_R > 0 THEN a.flight_number\\n                       END) / COUNT(DISTINCT a.flight_number)) * 100 AS delay_rate,\\n       AVG(CAST(f.TakeOffDelayTime_R AS DECIMAL)) AS average_delay_time\\nFROM airport_operations a\\nINNER JOIN flight_operation f ON a.flight_number = f.name\\nGROUP BY a.date;\\n```",
            "order": 1,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        }
    ]
}
// excel分析： 历史对话
const excel_analyze_chat = {
    "success": true,
    "err_code": null,
    "err_msg": null,
    "err_msg_zh": null,
    "data": [
        {
            "role": "human",
            "context": "分析表格并生成分析图表",
            "order": 1,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "ai",
            "context": "\n\n[对用户说的想法摘要]  \n1. 分析活动信息表中的活动生命周期和类型分布  \n2. 计算活动的持续时间、平均持续时间等指标  \n3. 统计不同类型活动的数量占比  \n4. 选择适合的图表类型展示分析结果  \n\n<api-call><name>response_line_chart</name><args><sql>\nSELECT \n  id,\n  activity_name,\n  activity_type,\n  start_time,\n  end_time,\n  create_time,\n  end_time - start_time AS duration\nFROM activity_info\nORDER BY duration DESC;\n</sql></args></api-call>\n\n<api-call><name>response_pie_chart</name><args><sql>\nSELECT \n  activity_type,\n  COUNT(*) AS count,\n  COUNT(*) / (SELECT COUNT(*) FROM activity_info) * 100 AS percentage\nFROM activity_info\nGROUP BY activity_type\nORDER BY count DESC;\n</sql></args></api-call>",
            "order": 1,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        },
        {
            "role": "view",
            "context": "```vis-thinking\\n\\n好的，我现在需要分析用户的问题，生成相应的DuckDB SQL查询，并选择合适的数据展示方式。用户的问题是“分析表格并生成分析图表”，所以我需要先理解用户的需求，然后根据提供的表结构信息来构建合适的SQL查询。\\n\\n首先，查看表结构。表名是activity_info，包含活动的基本信息，如id、名称、类型、描述、开始时间、结束时间和创建时间。用户要求分析这个表格并生成图表，所以我需要确定分析的目标和角度。\\n\\n考虑到活动的时间信息，分析活动的生命周期可能是一个好的切入点。活动的生命周期包括创建时间、开始时间、结束时间，这可以帮助用户了解活动的持续时间和活跃度。\\n\\n接下来，我思考如何计算活动的生命周期。可以使用end_time - start_time来计算活动的持续时间，但需要确保时间字段类型一致。DuckDB中，可以用end_time - start_time来得到持续时间，结果为间隔类型，可能需要转换为整数秒，以便后续分析。\\n\\n然后，分析活动的类型分布，看看哪种类型的活动最多。activity_type字段记录了活动的类型，比如2201代表满减活动，所以统计不同类型的活动数量可以帮助用户了解活动的类型分布。\\n\\n接下来，确定数据展示方式。对于活动的生命周期分析，使用柱状图（response_line_chart）可以展示每个活动的持续时间，而饼图（response_pie_chart）适合展示活动类型的比例分布。\\n\\n现在，构建SQL查询。首先，计算每个活动的持续时间，并转换为整数秒，然后计算平均持续时间。同时，统计每种活动类型的数量。需要注意的是，DuckDB中使用SUM(end_time - start_time)来计算总持续时间，但可能需要转换为整数类型，或者直接计算平均值。\\n\\n在编写SQL时，确保使用正确的列名和函数。例如，end_time和start_time都是TIMESTAMP类型，可以直接相减得到间隔。为了得到持续时间的平均值，可以使用AVG(end_time - start_time)。同时，统计活动类型的数量，使用COUNT(*) GROUP BY activity_type。\\n\\n最后，选择合适的数据展示方式。活动的生命周期分析适合使用柱状图，而活动类型分布适合饼图。因此，生成两个SQL查询，分别对应这两种图表。\\n\\n现在，将SQL查询分别包装在api-call中，选择合适的数据展示类型，并确保SQL语法正确，列名和函数使用正确。\\n\\n```\\n\\n\\n[对用户说的想法摘要]  \\n1. 分析活动信息表中的活动生命周期和类型分布  \\n2. 计算活动的持续时间、平均持续时间等指标  \\n3. 统计不同类型活动的数量占比  \\n4. 选择适合的图表类型展示分析结果  \\n\\n<chart-view content=\"{&quot;type&quot;: &quot;response_line_chart&quot;, &quot;sql&quot;: &quot; SELECT    id,   activity_name,   activity_type,   start_time,   end_time,   create_time,   end_time - start_time AS duration FROM activity_info ORDER BY duration DESC; &quot;, &quot;data&quot;: [{&quot;id&quot;: 1.0, &quot;activity_name&quot;: &quot;联想品牌优惠活动&quot;, &quot;activity_type&quot;: &quot;2201&quot;, &quot;start_time&quot;: &quot;2020-02-27T17:11:09&quot;, &quot;end_time&quot;: &quot;2020-02-29T17:11:12&quot;, &quot;create_time&quot;: &quot;2020-02-27T17:11:16&quot;, &quot;duration&quot;: &quot;P2DT0H0M3S&quot;}, {&quot;id&quot;: 2.0, &quot;activity_name&quot;: &quot;女生节优惠活动&quot;, &quot;activity_type&quot;: &quot;2201&quot;, &quot;start_time&quot;: &quot;2020-03-08T18:08:55&quot;, &quot;end_time&quot;: &quot;2020-03-08T18:09:04&quot;, &quot;create_time&quot;: &quot;2020-04-01T18:09:11&quot;, &quot;duration&quot;: &quot;P0DT0H0M9S&quot;}]}\">\\n</chart-view>\\n\\n<chart-view content=\"{&quot;type&quot;: &quot;response_pie_chart&quot;, &quot;sql&quot;: &quot; SELECT    activity_type,   COUNT(*) AS count,   COUNT(*) / (SELECT COUNT(*) FROM activity_info) * 100 AS percentage FROM activity_info GROUP BY activity_type ORDER BY count DESC; &quot;, &quot;data&quot;: [{&quot;activity_type&quot;: &quot;2201&quot;, &quot;count&quot;: 2, &quot;percentage&quot;: 100.0}]}\">\\n</chart-view>",
            "order": 1,
            "time_stamp": null,
            "model_name": "qwen3",
            "feedback": {}
        }
    ]
}

export {
  menus,chat_history
}